ASER Pakistan 2016

In this piece of paper, a set of data obtained from Annual Status of Education Report (ASER) is explored. The raw data was downloaded from the link here. https://palnetwork.org/aser-centre/

Preparation

Packages Used

library(tidyverse)
library(ggplot2)
library(ggthemes)
library(ggrepel)
library(gghighlight)
library(stringr)
library(dplyr)
library(sf)
library(scatterplot3d)
library(car)
library(ResourceSelection) #to excute Hosmer-Lemeshow test
## Warning: package 'ResourceSelection' was built under R version 4.0.3
# library(broom)
require(ggiraph)
## Warning: package 'ggiraph' was built under R version 4.0.3
require(ggiraphExtra)
# require(plyr)

Data Installation

provdist <- read.csv("aser/ASER2016ProvDist.csv")
school <- read.csv("aser/ASER2016GSchool.csv")
child <- read.csv("aser/ASER2016Child.csv")
pschool <- read.csv("aser/ASER2016PvtSchool.csv")
gschool <- read.csv("aser/ASER2016GSchool.csv")
parent <- read.csv("aser/ASER2016Parent.csv")
house <- read.csv("aser/ASER2016HouseholdIndicators.csv")
RegionName <- c("2" = "Panjab", 
                "3" = "Sindh", 
                "4" = "Balochistan", 
                "5" = "Khyber Pakhtunkhwa", 
                "6" = "Gilgit-Baltistan", 
                "7" = "Azad Jammu and Kashmir", 
                "8" = "Islamabad - ICT", 
                "9" = "Federally Administrated Tribal Areas")
Gender <- c("0" = "Male",
            "-1" = "Female")

Exploration

Checking Samplesizes

length(unique(child$CID))
## [1] 255196

The whole samplesize (the numebr of children) of this dataset is 255196.

child %>% 
  filter(DID == 266) %>% 
  summarize(N_hunza = length(unique(CID)))

The samplesize of Hunza alone is 1641.

Exploration in Hunza

Gender Proportion

child %>% 
  filter(DID == 266) %>% 
  summarize(gender_proportion = mean(C002))

-1: female, 0: male
gender_proportion = -0.5173675 means there are a little more girls in the dataset.

Age of Children (C001)

child %>% 
  filter(DID == 266) %>% 
  ggplot(aes(C001)) +
  geom_histogram()

Age is well sparsed

Eduation Status

child %>% 
  filter(DID == 266) %>% 
  ggplot(aes(C003)) +
  geom_histogram(bins = 3)

1 = never enrolled; 2 = drop-out; 3 = currently enrolled

Education Status by Gender

child %>% 
  filter(DID == 266) %>% 
  ggplot(aes(C003)) +
  geom_histogram(bins = 3, binwidth = 1) +
  facet_grid(~C002, labeller = labeller(C002 = Gender))

Both genders look pretty good interms of the absolute number of currently-enrolled-children

The Enrollment Rate by Gender

child %>% 
  filter(DID == 266) %>% 
  group_by(C002) %>% 
  mutate(enrollment_rate = mean(C003 == 3)) %>% 
  ggplot(aes(C002, enrollment_rate)) +
  geom_col() +
  scale_y_continuous() +
  geom_label(aes(label = enrollment_rate)) +
  scale_x_continuous(breaks = c(-1, 0), labels = c("Female", "Male"))

As a rate, both are doing pretty good

Currently-Enrolled: Institution Type (C006) in Hunza

1 = Government; 2 = Private; 3 = Madrasah(Conventional religious education) School; 4 = Other(Non formal education facility)

child %>% 
  filter(DID == 266) %>% 
  ggplot(aes(C006)) +
  geom_histogram()

Most children go to public schools or private schools

Within Gilgit-Baltistan

child %>% 
  filter(RID == 6) %>% 
  group_by(DID) %>% 
  mutate(Current_Enrollment_Rate = mean(C003 == 3)) %>% 
  ggplot(aes(DID, Current_Enrollment_Rate)) +
  geom_count() +
  scale_x_continuous(breaks = 260:266, labels = c("Gilgit", "Diamer", "Skardu", "Ghanshe", "Astore", "Ghizer", "Hunza-Nagar"))

Within Gilgit-Baltistan, Hunza is outperforming.

Basic Learning Levels: Reading in Local/National Language (C010)

1 = Begginer/Nothing; 2 = Letters; 3 = Words; 4 = Sentences; 5 = Story

child %>% 
  filter(DID == 266) %>% 
  ggplot(aes(C010)) +
  geom_histogram()

child %>% 
  filter(DID == 266) %>% 
  summarize(na = sum(is.na(C010)))

Basic Learning Levels by Age

child %>% 
  filter(DID == 266, C013 != c(3,4)) %>% 
  ggplot(aes(C010)) +
  geom_histogram() +
  facet_grid(~C001)

# children at he age of 3 and 4 are removed for they have not data

Basic Learning Levels by Gender

child %>% 
  filter(DID == 266) %>% 
  ggplot(aes(C010)) +
  geom_histogram() +
  facet_grid(~C002, labeller = labeller(C002 = Gender))

English Reading Levels (C013)

child %>% 
  filter(DID == 266, C013 != c(3,4)) %>% 
  ggplot(aes(C013)) +
  geom_histogram() +
  facet_grid(~C001)

# children at he age of 3 and 4 are removed for they have not data

English Reading Levels by Gender

child %>% 
  filter(DID == 266) %>% 
  ggplot(aes(C013)) +
  geom_histogram() +
  facet_grid(~C002, labeller = labeller(C002 = Gender))

Comparison between Other Region

Current Enrollment Rate

child %>% 
  group_by(DID) %>% 
  mutate(avg = round(mean(C003 == 3), digits = 2)) %>% 
  ungroup() %>% 
  ggplot(aes(avg)) +
  geom_histogram() +
  facet_grid(~RID, labeller = labeller(RID = RegionName)) +
  labs(title = "Current Enrollment Rate by Region")

Female Average Learning Levels (Age or other variables are NOT adjusted)

child %>% 
  filter(C002 == -1) %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C010, na.rm = TRUE)) %>% 
  ggplot(aes(DID, avg_learning, color = RID)) +
  geom_point() +
  geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE)

child %>% 
  filter(C002 == -1) %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C010, na.rm = TRUE)) %>% 
  ggplot(aes(DID, avg_learning, color = RID)) +
  geom_point() +
  geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE) +
  gghighlight(RID == 6)

It is interesting to note that Gilgit-Baltistan(RID==6) has a huge diversity in average learning levels of girls and Hunza(DID==266) is in the top group of all region.

Spatial Analysis

Districts Map

ica_df <- ica %>% 
  mutate(centroid = st_centroid(geometry),
    x = st_coordinates(centroid)[,1],
    y = st_coordinates(centroid)[,2]) %>% 
  as.data.frame()
## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.
## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data
ica_df <- ica_df %>% select(Province, Districts, x, y)
ica_df <- ica_df %>% summarize(Province = tolower(Province), Districts = tolower(Districts), x = x, y = y)

Child and ProvDist data combination

child_dname <- child %>% left_join(provdist[-1])
## Joining, by = "DID"
child_dname <- child_dname %>% mutate(dname = tolower(DNAME))
ica_df_3 <- ica_df %>% filter(Province == "sindh")

ica_df_3$Districts <- ica_df_3$Districts %>%  
  str_replace("ghotki", "gotki") %>%
  str_replace("mirpur khas", "mirpurkhas") %>% 
  str_replace("malir karachi", "karachi-malir-rural") %>% 
  str_replace("naushahro feroze", "nowshero feroze") %>% 
  str_replace("kambar shahdad kot", "qambar shahdadkot") %>% 
  str_replace("sujawal", "sajawal") %>% 
  str_replace("shaheed benazir abad", "shaheed benazirabad") %>% 
  str_replace("tando allahyar", "tando allah yar") %>% 
  as.vector()

child_dname_3 <- child_dname %>% filter(RNAME == "Sindh") %>% left_join(ica_df_3, by = c("dname" = "Districts"))

child_dname_3 %>% group_by(dname) %>% summarize(n = sum(x))
## `summarise()` ungrouping output (override with `.groups` argument)
ica_df_3

District IDs

ica %>% 
  mutate(centroid = st_centroid(geometry),
    x = st_coordinates(centroid)[,1],
    y = st_coordinates(centroid)[,2]) %>% 
    ggplot() +
  geom_sf() +
  geom_point(data = child_ica, aes(x, y, label = DID, color = factor(RID))) +
  geom_text(data = child_ica, aes(x, y, label = DID), size = 5, nudge_y = 0.2, check_overlap = TRUE)
## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.
## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data
## Warning: Ignoring unknown aesthetics: label
## Warning: Removed 8541 rows containing missing values (geom_point).
## Warning: Removed 8541 rows containing missing values (geom_text).

Gilgit-Baltistan Region

ica %>% 
  mutate(centroid = st_centroid(geometry),
    x = st_coordinates(centroid)[,1],
    y = st_coordinates(centroid)[,2]) %>% 
    ggplot() +
  geom_sf() +
  geom_point(data = child_ica, aes(x, y, label = DNAME, shape = factor(RID), color = factor(RID))) +
  geom_text(data = child_ica, aes(x, y, label = DNAME), size = 4.5, nudge_y = 0.2, check_overlap = TRUE) +
  scale_shape_manual(values = 0:8) +
  coord_sf(xlim = c(71, 77.9), ylim = c(34, 37.1))
## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.
## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data
## Warning: Ignoring unknown aesthetics: label
## Warning: Removed 8541 rows containing missing values (geom_point).
## Warning: Removed 8541 rows containing missing values (geom_text).

Latitudes and longitudes of Gilgit-Baltistan region

child_ica %>% 
  filter(RID == 6) %>% 
  summarise(xmin = min(x), xmax = max(x), ymin = min(y), ymax = max(y))

Average enrollment rate

ica %>% 
  mutate(centroid = st_centroid(geometry),
    x = st_coordinates(centroid)[,1],
    y = st_coordinates(centroid)[,2]) %>% 
    ggplot() +
  geom_sf() +
  geom_point(data = child_ica %>% group_by(DID) %>% 
               mutate(avg_enroll = mean(C003 == 3)), 
             aes(x, y, label = avg_enroll, color = avg_enroll))

Average enrollment rate of female children

ica %>% 
  mutate(centroid = st_centroid(geometry),
    x = st_coordinates(centroid)[,1],
    y = st_coordinates(centroid)[,2]) %>% 
    ggplot() +
  geom_sf() +
  geom_point(data = child_ica %>% filter(C002 == -1) %>% group_by(DID) %>% 
               mutate(avg_enroll = mean(C003 == 3)), 
             aes(x, y, label = avg_enroll, color = avg_enroll))
## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.
## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data
## Warning: Ignoring unknown aesthetics: label
## Warning: Removed 3566 rows containing missing values (geom_point).

ica %>% 
  mutate(centroid = st_centroid(geometry),
    x = st_coordinates(centroid)[,1],
    y = st_coordinates(centroid)[,2]) %>% 
    ggplot() +
  geom_sf() +
  geom_point(data = child_ica, aes(x, y, label = C010, color = C010))

  # geom_text(data = child_ica, aes(x, y, label = C010), check_overlap = TRUE, nudge_y = 1)

Traial: children, gender

ica %>% 
  mutate(centroid = st_centroid(geometry),
    x = st_coordinates(centroid)[,1],
    y = st_coordinates(centroid)[,2]) %>% 
    ggplot() +
  geom_sf() +
  geom_point(data = child_ica %>% 
               group_by(DID) %>%
               mutate(gender_ratio = mean(C002)), 
             aes(x, y, label = gender_ratio, color = gender_ratio))

0: male, -1: female

Thus, -0.5 indicates the complete gender parity

Children

Average Local/National Language Level Across Ages by Rgion

child_ica %>% 
  filter(C002 == c(0,-1)) %>% 
  group_by(DID, C001, C002) %>% 
  mutate(avg_local = mean(C010, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_local, color = factor(C002), group = DID)) +
  geom_line(show.legend = FALSE) +
  labs(x = "Age") +
  facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
  facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender))

Average Enrollment Rate

Across Ages by Rgion
child_ica %>% 
  filter(C002 == c(0,-1)) %>% 
  group_by(DID, C001, C002) %>% 
  mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
  geom_line(show.legend = FALSE) +
  labs(x = "Age") +
  facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
  facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender))

Hunza
child_ica %>% 
  filter(C002 != "NA", RID == 6, RID != "NA") %>% 
  group_by(DID, C001, C002) %>% 
  mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
  geom_line() +
  labs(x = "Age") +
  facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
  facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
  gghighlight(label_key = DNAME, calculate_per_facet = TRUE)

child_ica %>% 
  filter(C002 != "NA", RID == 6, RID != "NA") %>% 
  group_by(DID, C001, C002) %>% 
  mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
  geom_line() +
  labs(x = "Age") +
  facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
  facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
  gghighlight(DID == 266,label_key = DNAME, calculate_per_facet = TRUE)
## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...

Karachi
child_ica %>% 
  filter(C002 != "NA", RID == 3) %>% 
  group_by(DID, C001, C002) %>% 
  mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
  geom_line() +
  labs(x = "Age") +
  facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
  facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
  gghighlight(DID == c(315,316), label_key = DNAME, calculate_per_facet = TRUE)

children Education Stage
child_ica %>% 
  filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>% 
  group_by(C005) %>% 
  summarize(n = n()) %>%
  mutate(C005 = fct_reorder(C005, n)) %>% 
  ggplot(aes(C005, n)) +
  geom_col() +
  coord_flip()
## `summarise()` ungrouping output (override with `.groups` argument)

Hunza. Children Education Stage
child_ica %>% 
  filter(PR004 != "NA", PR009 != "NA", !is.na(C010), C002 != "NA", !is.na(C005), DID == 266) %>% 
  group_by(C005) %>% 
  summarize(n = n()) %>%
  mutate(C005 = fct_reorder(C005, n)) %>% 
  ggplot(aes(C005, n)) +
  geom_col() +
  coord_flip()
## `summarise()` ungrouping output (override with `.groups` argument)

Enrollment and Gender within Gilgit-Baltistan
child_ica %>% 
  filter(!is.na(C002), RID == 6) %>% 
  group_by(DID, C001, C002) %>% 
  mutate(enroll = mean(C003 == 3)) %>% 
  ggplot(aes(C001, enroll, group = C002, color = factor(C002))) +
  geom_line() +
  facet_wrap(.~DID)

Education Stages
child_ica %>% 
  group_by(DID) %>% 
  mutate(n = length(C003),
            dropout = sum(C003 == 2),
            dropout_ratio = mean(C003 == 2),
            never = sum(C003 == 1),
            never_ratio = mean(C003 == 1)) %>% 
  ggplot(aes(DID, never_ratio, color = factor(RID))) +
  geom_point()

Drop-Out C003 == 2
child_ica %>% 
  group_by(DID) %>% 
  summarize(dropout = mean(C003 == 2)) %>% 
  ggplot(aes(factor(DID), dropout)) +
  geom_point()
## `summarise()` ungrouping output (override with `.groups` argument)

child_ica %>% 
  filter(!is.na(C002)) %>% 
  group_by(DID, C002) %>% 
  summarize(dropout = mean(C003 == 2)) %>% 
  ggplot(aes(factor(DID), dropout, color = factor(C002))) +
  geom_point()
## `summarise()` regrouping output by 'DID' (override with `.groups` argument)

Never Enrolled C003 == 1
child_ica %>% 
  group_by(DID) %>% 
  summarize(neverEnr = mean(C003 == 1)) %>% 
  ggplot(aes(factor(DID), neverEnr)) +
  geom_point()
## `summarise()` ungrouping output (override with `.groups` argument)

child_ica %>% 
  filter(!is.na(C002)) %>% 
  group_by(DID, C002) %>% 
  summarize(neverEnr = mean(C003 == 1)) %>% 
  ggplot(aes(factor(DID), neverEnr, color = factor(C002))) +
  geom_point()
## `summarise()` regrouping output by 'DID' (override with `.groups` argument)

Hunza. Never Enrolled C003_1_01
child_ica %>% 
  filter(!is.na(C002)) %>% 
  filter(DID == 266) %>% 
  summarize(n_neverEnr = sum(C003 == 1), 
            ratio = mean(C003 == 1))
child_ica %>% 
  filter(DID == 266, !is.na(C002)) %>%
  group_by(C002) %>% 
  summarize(n_neverEnr = sum(C003 == 1), 
            ratio = mean(C003 == 1))
## `summarise()` ungrouping output (override with `.groups` argument)

Child & Parents

Mother Enrollment by District
child_ica %>%
  filter(RID == 6, PR004 != "NA") %>% 
  group_by(DID, PR004) %>% 
  mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>% 
  ggplot(aes(DID, avg_enrollment, fill = factor(PR004))) +
  geom_col(aes(factor(DID), avg_enrollment, fill = factor(PR004)), position = "dodge")

Within Gilgit-Baltistan, Hunza is the only district where there are more women who have experience of education than those who have never enrolled in schools.

Father Enrollment by District
child_ica %>%
  filter(RID == 6, PR009 != "NA") %>% 
  group_by(DID, PR009) %>% 
  mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>% 
  ggplot(aes(DID, avg_enrollment, group = PR009, fill = factor(PR009))) +
  geom_col(aes(factor(DID), avg_enrollment), position = "dodge")

Mother’s Education and Children Learning Level by Region. boxplot
child_ica %>% 
  filter(PR009 != "NA", PR004 != "NA") %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C003 == 3)) %>% 
  ggplot(aes(PR004, avg_learning, group = PR004)) +
  geom_boxplot(aes(PR004, avg_learning)) +
  facet_grid(.~RID)

Father’s Education and Children Learning Level by Region. boxplot
child_ica %>% 
  filter(PR009 != "NA", PR004 != "NA") %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C003 == 3)) %>% 
  ggplot(aes(PR009, avg_learning, group = PR009)) +
  geom_boxplot(aes(PR009, avg_learning)) +
  facet_grid(.~RID)

Mother’s Education and Children Learning Level by District
child_ica %>% 
  filter(PR009 != "NA", PR004 != "NA") %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), mother_enrollment = mean(PR004, na.rm = TRUE)) %>% 
  ggplot(aes(mother_enrollment, avg_learning)) +
  geom_point()

child_ica %>% 
  filter(PR009 != "NA", PR004 != "NA") %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), mother_enrollment = mean(PR004, na.rm = TRUE)) %>% 
  ungroup() %>% 
  summarize(r = cor(mother_enrollment, avg_learning))
Father’s Education and Children Learning Level by District
child_ica %>% 
  filter(PR009 != "NA", PR004 != "NA") %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), father_enrollment = mean(PR009, na.rm = TRUE)) %>% 
  ggplot(aes(father_enrollment, avg_learning)) +
  geom_point()

child_ica %>% 
  filter(PR009 != "NA", PR004 != "NA") %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C003 == 3), father_enrollment = mean(PR009)) %>% 
  ungroup() %>% 
  summarize(r = cor(father_enrollment, avg_learning))
Hunza. Age, Learning Level, Mother’s Education,
child_ica %>% 
  filter(PR009 != "NA", PR004 != "NA", C003 != "NA", DID == 266, C001 != "NA") %>% 
  group_by(PR004, C001) %>% 
  mutate(avg_learning = mean(C010, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_learning, group = PR004, color = factor(PR004))) +
  geom_line(aes(C001, avg_learning))
## Warning: Removed 200 row(s) containing missing values (geom_path).

Hunza. Age, Learning Level, Father’s Education
child_ica %>% 
  filter(PR009 != "NA", PR004 != "NA", C003 != "NA", DID == 266, C001 != "NA") %>% 
  group_by(PR009, C001) %>% 
  mutate(avg_learning = mean(C010, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_learning, group = PR009, color = factor(PR009))) +
  geom_line(aes(C001, avg_learning))
## Warning: Removed 200 row(s) containing missing values (geom_path).

Gilgit-Baltistan. Age, Learning Level, Mother’s Education
child_ica %>% 
  filter(PR009 != "NA", 
         PR004 != "NA", 
         C003 != "NA", 
         C001 != "NA",
         RID == 6) %>% 
  group_by(DID, PR004, C001) %>% 
  mutate(avg_learning = mean(C010, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_learning, group = PR004, color = factor(PR004))) +
  geom_line(aes(C001, avg_learning)) +
  facet_grid(.~DID) +
  facet_wrap(.~DID)
## Warning: Removed 226 row(s) containing missing values (geom_path).

Gilgit-Baltistan. Age, Learning Level, Father’s Education
child_ica %>% 
  filter(PR009 != "NA", 
         PR004 != "NA", 
         C003 != "NA", 
         C001 != "NA",
         RID == 6) %>% 
  group_by(DID, PR009, C001) %>% 
  mutate(avg_learning = mean(C010, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_learning, group = PR009, color = factor(PR009))) +
  geom_line(aes(C001, avg_learning)) +
  facet_grid(.~DID) +
  facet_wrap(.~DID)
## Warning: Removed 226 row(s) containing missing values (geom_path).

Gilgit-Baltistan. Age, Enrollment, Mother’s Education
child_ica %>% 
  filter(PR009 != "NA", 
         PR004 != "NA", 
         C003 != "NA", 
         C001 != "NA",
         RID == 6) %>% 
  group_by(DID, PR004, C001) %>% 
  mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_enrollment, group = PR004, color = factor(PR004))) +
  geom_line(aes(C001, avg_enrollment)) +
  facet_grid(.~DID) +
  facet_wrap(.~DID)

Gilgit-Baltistan. Age, Enrollment, Father’s Education
child_ica %>%
  filter(PR009 != "NA", 
         PR004 != "NA", 
         C003 != "NA", 
         C001 != "NA",
         RID == 6) %>% 
  group_by(DID, PR009, C001) %>% 
  mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>% 
  ggplot(aes(C001, avg_enrollment, group = PR009, color = factor(PR009))) +
  geom_line(aes(C001, avg_enrollment)) +
  facet_grid(.~DID) +
  facet_wrap(.~DID)

Mothers Education Level
child_ica %>% 
  filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>% 
  ggplot(aes(PR005)) +
  geom_histogram(stat = "count") +
  coord_flip()
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Summary
child_ica %>% 
  ggplot(aes(PR006)) +
  geom_histogram(stat = "count") +
  coord_flip()
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Fathers Education Level
child_ica %>% 
  filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>%
  ggplot(aes(PR010)) +
  geom_histogram(stat = "count") +
  coord_flip()
## Warning: Ignoring unknown parameters: binwidth, bins, pad

###### Summary

child_ica %>% 
  ggplot(aes(PR011)) +
  geom_histogram(stat = "count") +
  coord_flip()
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Mothers Education Level by Region
child_ica %>%
  group_by(DID, PR006) %>% 
  ggplot(aes(PR006)) +
  geom_histogram(aes(PR006), stat = "count") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 5)) +
  facet_grid(.~RID)
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Mother Enrollment by District
child_ica %>% 
  filter(PR004 != "NA", PR009 != "NA", C010 != "NA") %>% 
  group_by(DID) %>% 
  mutate(mother_edu_rate = sum(PR004 == -1)/length(PR004)) %>% 
  ggplot(aes(DID, mother_edu_rate, shape = factor(RID), color = factor(RID))) +
  geom_point() +
  scale_shape_manual(values = 0:8)

Gilgit-Baltistan. Average Mother Education Experience, House Type
child_ica %>% 
  filter(PR004 != "NA", PR009 != "NA", C010 != "NA") %>% 
  group_by(DID) %>% 
  mutate(mother_edu_rate = sum(PR004 == -1)/length(PR004)) %>% ungroup() %>% 
  ggplot(aes(factor(RID), mother_edu_rate, group = RID)) +
  geom_violin()

Hunza. Father Enroll Ratio
child_ica %>% 
  filter(DID == 266, !is.na(PR009)) %>% 
  summarize(father_edu_ratio = sum(PR009 == -1)/length(PR009))
Father Enroll Ratio
child_ica %>% 
  filter(!is.na(PR009)) %>% 
  summarize(father_edu_ratio = sum(PR009 == -1)/length(PR009))
Father Enroll ratio by district
child_ica %>% 
  filter(!is.na(PR009)) %>% 
  group_by(DID) %>% 
  mutate(father_edu_ratio = sum(PR009 == -1)/length(PR009)) %>% 
  ggplot(aes(factor(DID), father_edu_ratio)) +
  geom_point()

mother enroll ratio by district
child_ica %>% 
  filter(!is.na(PR009), !is.na(PR004)) %>% 
  group_by(DID) %>% 
  mutate(mother_edu_ratio = sum(PR004 == -1)/length(PR004)) %>% 
  ggplot(aes(factor(DID), mother_edu_ratio)) +
  geom_point()

Child, Household

Number of Children in a household
child_ica %>%
  filter(!is.na(PR004), !is.na(PR009)) %>% 
  filter(HHID == 96546) %>% 
  select(CID, PRID, C002, C001, PR001, VID, C003, DID, RID)
child_ica %>% 
  group_by(HHID) %>%
  mutate(n = n()) %>% 
  ggplot(aes(factor(n), fill = factor(RID))) +
  geom_bar(position = "dodge") +
  facet_grid(.~RID) +
  facet_wrap(.~RID)

house type ratio by region
child_ica %>% 
  ggplot(aes(x = factor(DID), y = factor(H002), fill = factor(H002))) +
  geom_bar(aes(x = factor(DID), y = factor(H002)), stat = "identity", position = "fill") +
  facet_grid(.~RID, scales = "free") +
  facet_wrap(.~RID, scales = "free") +
  theme(axis.text.x = element_text(angle = 90, size = 7, hjust = 1)) +
  coord_cartesian(ylim = c(0,1), clip = "off", expand = FALSE)

Gilgit-Baltistan. house type
child_ica %>% 
  filter(C003 != "NA", RID == 6) %>% 
  group_by(DID, H002) %>% 
  mutate(avg_enrollment = mean(C003 == 3)) %>% 
  ggplot(aes(factor(DID), avg_enrollment, group = H002, fill = factor(H002))) +
  geom_col(position = "dodge")

Hunza. house type
child_ica %>% left_join(house, by = "HHID") %>% 
  filter(DID.x == 266) %>% 
  ggplot(aes(H002.x)) +
  geom_histogram(aes(H002.x), stat = "count")
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Average Number of Children in a household

child_ica %>% 
  group_by(HHID) %>% 
  mutate(n_children = length(unique(CID))) %>% 
  ungroup() %>% 
  group_by(DID) %>% 
  mutate(avg_n_children = mean(n_children)) %>% 
  ggplot(aes(DID, avg_n_children, color = factor(RID))) +
  geom_point()

child_ica %>% 
  ggplot(aes(C005)) +
  geom_histogram(stat = "count") +
  coord_flip()
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Hunza, house type
child_ica %>% 
  filter(DID == 266) %>% 
  group_by(H002) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(H002, n)) +
  geom_col() +
  geom_text(aes(label = n), vjust = 1.5, color = "white") +
  xlab("House Type") +
  ylab("Number of Households")
## `summarise()` ungrouping output (override with `.groups` argument)

Hunza, number of children in households
child_ica%>% 
  filter(DID == 266) %>% 
  group_by(HHID) %>%
  summarize(n = length(unique(CID))) %>% 
  ungroup() %>% 
  group_by(n) %>% 
  summarize(num = n()) %>% 
  ggplot(aes(factor(n), num)) +
  geom_col() +
  geom_text(aes(label = num), vjust = -0.5) +
  xlab("Number of Children in Each Household") +
  ylab("Number of Households") 
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)

VID

Gilgit (DID == 260)
child_ica %>%
  filter(!is.na(C002), DID == 260) %>% 
  group_by(VID, C002) %>% 
  summarize(avg_enroll = mean(C003 == 3)) %>% 
  ggplot(aes(VID, avg_enroll, color = factor(C002))) +
  geom_point()
## `summarise()` regrouping output by 'VID' (override with `.groups` argument)

Preparation for Logistic Regression Analysis

Making Dataframe With Dummy Variables

child_ica_dummy <- child_ica %>% filter(!is.na(C002), !is.na(C003), !is.na(PR004), !is.na(PR009))
child_ica_dummy$C002_01 <- ifelse(child_ica_dummy$C002 == -1, 1, 0)
child_ica_dummy$C003_01 <- ifelse(child_ica_dummy$C003 == 3, 1, 0)     # currently-enrolled
child_ica_dummy$C003_1_01 <- ifelse(child_ica_dummy$C003 == 1, 1, 0)   # never-enrolled
child_ica_dummy$C003_2_01 <- ifelse(child_ica_dummy$C003 == 2, 1, 0)   # drop-out
child_ica_dummy$PR004_01 <- ifelse(child_ica_dummy$PR004 == -1, 1, 0)
child_ica_dummy$PR009_01 <- ifelse(child_ica_dummy$PR009 == -1, 1, 0)
child_ica_dummy$PR004_PR009_01 <- ifelse(
  child_ica_dummy$PR004 == -1 | child_ica_dummy$PR009 == -1, 1, 0)


child_ica_dummy$PR004_only_01 <- ifelse(
  child_ica_dummy$PR004 == -1 & child_ica_dummy$PR009 == 0, 1, 0)
child_ica_dummy$PR009_only_01 <- ifelse(
  child_ica_dummy$PR004 == 0 & child_ica_dummy$PR009 == -1, 1, 0)
child_ica_dummy$PR004_PR009_both_01 <- ifelse(
  child_ica_dummy$PR004 == -1 & child_ica_dummy$PR009 == -1, 1, 0)
n_children_in_household <- child_ica_dummy %>% 
  group_by(HHID) %>% 
  summarize(n_children_in_household = length(unique(CID)))
## `summarise()` ungrouping output (override with `.groups` argument)
child_ica_dummy <- child_ica_dummy %>% left_join(n_children_in_household)
## Joining, by = "HHID"
child_ica_dummy$H002_1_01 <- ifelse(child_ica_dummy$H002 == 1, 1, 0)
child_ica_dummy$H002_2_01 <- ifelse(child_ica_dummy$H002 == 2, 1, 0)
child_ica_dummy$H002_3_01 <- ifelse(child_ica_dummy$H002 == 3, 1, 0)

Create dummy variable with regard to region

child_ica_dummy <- child_ica_dummy
child_ica_dummy$Panjab <- ifelse(child_ica_dummy$RID == 2, 1, 0)
child_ica_dummy$Sindh <- ifelse(child_ica_dummy$RID == 3, 1, 0)
child_ica_dummy$Balochistan <- ifelse(child_ica_dummy$RID == 4, 1, 0)
child_ica_dummy$Khyber_Pakhtunkhwa <- ifelse(child_ica_dummy$RID == 5, 1, 0)
child_ica_dummy$Gilgit_Baltistan <- ifelse(child_ica_dummy$RID == 6, 1, 0)
child_ica_dummy$Azad_Jammu_and_Kashmir <- ifelse(child_ica_dummy$RID == 7, 1, 0)
child_ica_dummy$Islamabad_ICT <- ifelse(child_ica_dummy$RID == 8, 1, 0)
child_ica_dummy$Federally_Administrated_Tribal_Areas <- ifelse(child_ica_dummy$RID == 9, 1, 0)

Eliminated NAs

child_ica %>% 
  summarize(C002_na = sum(is.na(C002)),
            C003_na = sum(is.na(C003)),
            PR004_na = sum(is.na(PR004)),
            PR009_na = sum(is.na(PR009)))

Eliminated Rows in Total

data.frame(original_rows = nrow(child_ica),
           eliminated_rows = nrow(child_ica) - nrow(child_ica_dummy),
           ratio = (nrow(child_ica)-nrow(child_ica_dummy))/nrow(child_ica))

Eliminated NAs Hunza

child_ica %>% 
  filter(DID == 266) %>% 
  summarize(C002_na = sum(is.na(C002)),
            C003_na = sum(is.na(C003)),
            PR004_na = sum(is.na(PR004)),
            PR009_na = sum(is.na(PR009)))

Eliminated Rows in Total Hunza

data.frame(original_rows = nrow(child_ica %>% filter(DID == 266)),
           eliminated_rows = nrow(child_ica %>% filter(DID == 266)) - nrow(child_ica_dummy %>% filter(DID == 266)),
           ratio = (nrow(child_ica %>% filter(DID == 266) %>% filter(DID == 266))-nrow(child_ica_dummy %>% filter(DID == 266)))/nrow(child_ica %>% filter(DID == 266)))

Generalized Linear Models

GLM C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01

glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, 
##     family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8850  -1.2383   0.6563   0.8621   1.3071  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              0.934846   0.013780   67.84   <2e-16 ***
## n_children_in_household -0.055206   0.002935  -18.81   <2e-16 ***
## C002_01                 -0.572083   0.009041  -63.28   <2e-16 ***
## PR004_PR009_01           0.711645   0.009062   78.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 289072  on 245743  degrees of freedom
## AIC: 289080
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 490.13, df = 8, p-value < 2.2e-16

Hunza. C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01

glm_child_hunza <- glm(C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))

ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, 
##     family = "binomial", data = child_ica_dummy %>% filter(DID == 
##         266))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5255   0.3335   0.3611   0.4126   0.6056  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              2.74787    0.35406   7.761 8.43e-15 ***
## n_children_in_household -0.16354    0.07071  -2.313   0.0207 *  
## C002_01                  0.12227    0.19347   0.632   0.5274    
## PR004_PR009_01           0.44052    0.21451   2.054   0.0400 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 838.79  on 1609  degrees of freedom
## Residual deviance: 826.06  on 1606  degrees of freedom
## AIC: 834.06
## 
## Number of Fisher Scoring iterations: 5
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 9.9762, df = 8, p-value = 0.2667
exponential transformation
exp(glm_child_hunza$coefficients)
##             (Intercept) n_children_in_household                 C002_01 
##              15.6093953               0.8491308               1.1300567 
##          PR004_PR009_01 
##               1.5535098
confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)
## Waiting for profiling to be done...
##                              2.5 %      97.5 %
## (Intercept)              2.0690843  3.45821705
## n_children_in_household -0.3018419 -0.02433219
## C002_01                 -0.2575389  0.50262655
## PR004_PR009_01           0.0115443  0.85445608
exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))
## Waiting for profiling to be done...
##                             2.5 %     97.5 %
## (Intercept)             7.9175694 31.7602989
## n_children_in_household 0.7394550  0.9759614
## C002_01                 0.7729515  1.6530574
## PR004_PR009_01          1.0116112  2.3500958
AIC
extractAIC(glm_child_hunza)
## [1]   4.0000 834.0574
BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))
## [1]   4.0000 855.5934
effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anova
variables selection
step(glm_child_hunza_null, direction = "both", 
     scope = (~ C001 + C002_01 + PR004_01 + PR009_01))
## Start:  AIC=840.79
## C003_01 ~ 1
## 
##            Df Deviance    AIC
## + C001      1   732.49 736.49
## + PR004_01  1   824.25 828.25
## + PR009_01  1   830.82 834.82
## <none>          838.79 840.79
## + C002_01   1   838.58 842.58
## 
## Step:  AIC=736.49
## C003_01 ~ C001
## 
##            Df Deviance    AIC
## + PR004_01  1   707.11 713.11
## + PR009_01  1   717.26 723.26
## <none>          732.49 736.49
## + C002_01   1   732.49 738.49
## - C001      1   838.79 840.79
## 
## Step:  AIC=713.11
## C003_01 ~ C001 + PR004_01
## 
##            Df Deviance    AIC
## <none>          707.11 713.11
## + PR009_01  1   705.74 713.74
## + C002_01   1   707.11 715.11
## - PR004_01  1   732.49 736.49
## - C001      1   824.25 828.25
## 
## Call:  glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial", 
##     data = child_ica_dummy %>% filter(DID == 266))
## 
## Coefficients:
## (Intercept)         C001     PR004_01  
##     -0.4789       0.3039       1.0316  
## 
## Degrees of Freedom: 1609 Total (i.e. Null);  1607 Residual
## Null Deviance:       838.8 
## Residual Deviance: 707.1     AIC: 713.1
multicolinearity
vif(glm_child_hunza)
## n_children_in_household                 C002_01          PR004_PR009_01 
##                1.087196                1.006453                1.080729

GLM C003_01 ~ n_children_in_household + H002 + PR004_PR009_01

glm_child <- glm(C003_01 ~ n_children_in_household + H002 + PR004_PR009_01, family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + PR004_PR009_01, 
##     family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.927  -1.297   0.723   0.869   1.186  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              0.094686   0.016614   5.699  1.2e-08 ***
## n_children_in_household -0.049487   0.002933 -16.871  < 2e-16 ***
## H002                     0.379659   0.006366  59.636  < 2e-16 ***
## PR004_PR009_01           0.502871   0.009477  53.061  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 289446  on 245743  degrees of freedom
## AIC: 289454
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16

Hunza. C003_01 ~ n_children_in_household + H002 + PR004_PR009_01

glm_child_hunza <- glm(C003_01 ~ n_children_in_household + H002 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))

ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + PR004_PR009_01, 
##     family = "binomial", data = child_ica_dummy %>% filter(DID == 
##         266))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6103   0.3376   0.3660   0.4038   0.5903  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              3.23266    0.49678   6.507 7.65e-11 ***
## n_children_in_household -0.16663    0.07002  -2.380   0.0173 *  
## H002                    -0.20362    0.16445  -1.238   0.2157    
## PR004_PR009_01           0.51067    0.22107   2.310   0.0209 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 838.79  on 1609  degrees of freedom
## Residual deviance: 824.91  on 1606  degrees of freedom
## AIC: 832.91
## 
## Number of Fisher Scoring iterations: 5
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 15.469, df = 8, p-value = 0.05064
exponential transformation
exp(glm_child_hunza$coefficients)
##             (Intercept) n_children_in_household                    H002 
##              25.3469307               0.8465115               0.8157746 
##          PR004_PR009_01 
##               1.6664083
confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)
## Waiting for profiling to be done...
##                               2.5 %      97.5 %
## (Intercept)              2.27327932  4.22193092
## n_children_in_household -0.30339827 -0.02854365
## H002                    -0.52934555  0.11610748
## PR004_PR009_01           0.06943756  0.93802587
exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))
## Waiting for profiling to be done...
##                             2.5 %     97.5 %
## (Intercept)             9.7111948 68.1649785
## n_children_in_household 0.7383050  0.9718599
## H002                    0.5889903  1.1231166
## PR004_PR009_01          1.0719051  2.5549327
AIC
extractAIC(glm_child_hunza)
## [1]   4.0000 832.9071
BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))
## [1]   4.0000 854.4431
effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anova
variables selection
step(glm_child_hunza_null, direction = "both", 
     scope = (~ C001 + C002_01 + PR004_01 + PR009_01))
## Start:  AIC=840.79
## C003_01 ~ 1
## 
##            Df Deviance    AIC
## + C001      1   732.49 736.49
## + PR004_01  1   824.25 828.25
## + PR009_01  1   830.82 834.82
## <none>          838.79 840.79
## + C002_01   1   838.58 842.58
## 
## Step:  AIC=736.49
## C003_01 ~ C001
## 
##            Df Deviance    AIC
## + PR004_01  1   707.11 713.11
## + PR009_01  1   717.26 723.26
## <none>          732.49 736.49
## + C002_01   1   732.49 738.49
## - C001      1   838.79 840.79
## 
## Step:  AIC=713.11
## C003_01 ~ C001 + PR004_01
## 
##            Df Deviance    AIC
## <none>          707.11 713.11
## + PR009_01  1   705.74 713.74
## + C002_01   1   707.11 715.11
## - PR004_01  1   732.49 736.49
## - C001      1   824.25 828.25
## 
## Call:  glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial", 
##     data = child_ica_dummy %>% filter(DID == 266))
## 
## Coefficients:
## (Intercept)         C001     PR004_01  
##     -0.4789       0.3039       1.0316  
## 
## Degrees of Freedom: 1609 Total (i.e. Null);  1607 Residual
## Null Deviance:       838.8 
## Residual Deviance: 707.1     AIC: 713.1
# step(glm_child_null, direction = "both", 
#      scope = (~ C001 + C002 + PR004_01 + PR009_01 + 
#                 Panjab + Sindh + Balochistan + Khyber_Pakhtunkhwa + Gilgit_Baltistan + 
#                 Azad_Jammu_and_Kashmir + Islamabad_ICT))
multicolinearity
vif(glm_child_hunza)
## n_children_in_household                    H002          PR004_PR009_01 
##                1.083416                1.088995                1.147110

GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + H002

glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + H002, family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     H002, family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.927  -1.297   0.723   0.869   1.186  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              0.094686   0.016614   5.699  1.2e-08 ***
## n_children_in_household -0.049487   0.002933 -16.871  < 2e-16 ***
## PR004_PR009_01           0.502871   0.009477  53.061  < 2e-16 ***
## H002                     0.379659   0.006366  59.636  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 289446  on 245743  degrees of freedom
## AIC: 289454
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16

Hunza. C003_01 ~ n_children_in_household + PR004_PR009_01 + H002

glm_child_hunza <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + H002, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))

ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     H002, family = "binomial", data = child_ica_dummy %>% filter(DID == 
##     266))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6103   0.3376   0.3660   0.4038   0.5903  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              3.23266    0.49678   6.507 7.65e-11 ***
## n_children_in_household -0.16663    0.07002  -2.380   0.0173 *  
## PR004_PR009_01           0.51067    0.22107   2.310   0.0209 *  
## H002                    -0.20362    0.16445  -1.238   0.2157    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 838.79  on 1609  degrees of freedom
## Residual deviance: 824.91  on 1606  degrees of freedom
## AIC: 832.91
## 
## Number of Fisher Scoring iterations: 5
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16

Multicolinearity house type and parent edu?

child_ica_dummy %>% 
  group_by(H002) %>% 
  mutate(n_parents = length(PR004_PR009_01))%>% 
  ggplot(aes(H002, n_parents, fill = factor(PR004_PR009_01))) +
  geom_col(position = "stack") +
  xlab("Type of Houses") +
  ylab("Number of Parents") +
  labs(fill = "Parents Education\n(at least one of them, primary or more)")
The more educated the parents are, the better house they are living in.

GLM C003_01 ~ n_children_in_household + H002 + C002_01

glm_child <- glm(C003_01 ~ n_children_in_household + H002 + C002_01, family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01, 
##     family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0122  -1.2391   0.7184   0.8909   1.2724  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              0.424111   0.016894   25.10   <2e-16 ***
## n_children_in_household -0.052085   0.002941  -17.71   <2e-16 ***
## H002                     0.503563   0.006129   82.16   <2e-16 ***
## C002_01                 -0.575354   0.009055  -63.54   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 288199  on 245743  degrees of freedom
## AIC: 288207
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 818.15, df = 8, p-value < 2.2e-16

Hunza. C003_01 ~ n_children_in_household + H002 + C002_01

glm_child_hunza <- glm(C003_01 ~ n_children_in_household + H002 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))

ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01, 
##     family = "binomial", data = child_ica_dummy %>% filter(DID == 
##         266))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5804   0.3373   0.3731   0.4124   0.5843  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              3.48657    0.48393   7.205 5.82e-13 ***
## n_children_in_household -0.20822    0.06887  -3.023   0.0025 ** 
## H002                    -0.11589    0.15847  -0.731   0.4646    
## C002_01                  0.13022    0.19320   0.674   0.5003    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 838.79  on 1609  degrees of freedom
## Residual deviance: 829.57  on 1606  degrees of freedom
## AIC: 837.57
## 
## Number of Fisher Scoring iterations: 5
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 12.015, df = 8, p-value = 0.1505
exponential transformation
exp(glm_child_hunza$coefficients)
##             (Intercept) n_children_in_household                    H002 
##              32.6735833               0.8120302               0.8905720 
##                 C002_01 
##               1.1390753
confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)
## Waiting for profiling to be done...
##                              2.5 %      97.5 %
## (Intercept)              2.5525708  4.45124970
## n_children_in_household -0.3424699 -0.07212852
## H002                    -0.4291928  0.19275626
## C002_01                 -0.2490334  0.51006070
exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))
## Waiting for profiling to be done...
##                              2.5 %     97.5 %
## (Intercept)             12.8400705 85.7340185
## n_children_in_household  0.7100145  0.9304113
## H002                     0.6510344  1.2125872
## C002_01                  0.7795540  1.6653923
AIC
extractAIC(glm_child_hunza)
## [1]   4.0000 837.5663
BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))
## [1]   4.0000 859.1022
effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anova
variables selection
step(glm_child_hunza_null, direction = "both", 
     scope = (~ C001 + C002_01 + PR004_01 + PR009_01))
## Start:  AIC=840.79
## C003_01 ~ 1
## 
##            Df Deviance    AIC
## + C001      1   732.49 736.49
## + PR004_01  1   824.25 828.25
## + PR009_01  1   830.82 834.82
## <none>          838.79 840.79
## + C002_01   1   838.58 842.58
## 
## Step:  AIC=736.49
## C003_01 ~ C001
## 
##            Df Deviance    AIC
## + PR004_01  1   707.11 713.11
## + PR009_01  1   717.26 723.26
## <none>          732.49 736.49
## + C002_01   1   732.49 738.49
## - C001      1   838.79 840.79
## 
## Step:  AIC=713.11
## C003_01 ~ C001 + PR004_01
## 
##            Df Deviance    AIC
## <none>          707.11 713.11
## + PR009_01  1   705.74 713.74
## + C002_01   1   707.11 715.11
## - PR004_01  1   732.49 736.49
## - C001      1   824.25 828.25
## 
## Call:  glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial", 
##     data = child_ica_dummy %>% filter(DID == 266))
## 
## Coefficients:
## (Intercept)         C001     PR004_01  
##     -0.4789       0.3039       1.0316  
## 
## Degrees of Freedom: 1609 Total (i.e. Null);  1607 Residual
## Null Deviance:       838.8 
## Residual Deviance: 707.1     AIC: 713.1
# step(glm_child_null, direction = "both", 
#      scope = (~ C001 + C002 + PR004_01 + PR009_01 + 
#                 Panjab + Sindh + Balochistan + Khyber_Pakhtunkhwa + Gilgit_Baltistan + 
#                 Azad_Jammu_and_Kashmir + Islamabad_ICT))
multicolinearity
vif(glm_child_hunza)
## n_children_in_household                    H002                 C002_01 
##                1.021627                1.015482                1.006245

GLM C003_01 ~ n_children_in_household + C002_01 + H002

glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + H002, family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + H002, 
##     family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0122  -1.2391   0.7184   0.8909   1.2724  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              0.424111   0.016894   25.10   <2e-16 ***
## n_children_in_household -0.052085   0.002941  -17.71   <2e-16 ***
## C002_01                 -0.575354   0.009055  -63.54   <2e-16 ***
## H002                     0.503563   0.006129   82.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 288199  on 245743  degrees of freedom
## AIC: 288207
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 818.15, df = 8, p-value < 2.2e-16

Hunza. C003_01 ~n_children_in_household + C002_01 + H002

glm_child_hunza <- glm(C003_01 ~n_children_in_household + C002_01 + H002, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))

ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + H002, 
##     family = "binomial", data = child_ica_dummy %>% filter(DID == 
##         266))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5804   0.3373   0.3731   0.4124   0.5843  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              3.48657    0.48393   7.205 5.82e-13 ***
## n_children_in_household -0.20822    0.06887  -3.023   0.0025 ** 
## C002_01                  0.13022    0.19320   0.674   0.5003    
## H002                    -0.11589    0.15847  -0.731   0.4646    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 838.79  on 1609  degrees of freedom
## Residual deviance: 829.57  on 1606  degrees of freedom
## AIC: 837.57
## 
## Number of Fisher Scoring iterations: 5
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 12.015, df = 8, p-value = 0.1505

C002_01 chi Districts in Gilgit-Baltistan

child_ica_dummy %>% filter(RID == 6) %>% summarize(unique(DID))
sapply(260:266, function(dist){
  glm_child_dist <- glm(C003_01 ~ C002_01, family = "binomial", data = child_ica_dummy %>% filter(RID == 6, DID == dist))
  glm_child_dist_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(RID == 6, DID == dist))
anova <- anova(glm_child_dist_null, glm_child_dist, test = "Chisq")
data.frame(result = anova$`Pr(>Chi)`[2], DID = dist, chi_0.05 = anova$`Pr(>Chi)`[2] >= 0.05)
})
##          [,1]       [,2]          [,3]       [,4]       [,5]      [,6]     
## result   0.02323511 1.711351e-125 0.02518717 0.04226537 0.1507538 0.7348869
## DID      260        261           262        263        264       265      
## chi_0.05 FALSE      FALSE         FALSE      FALSE      TRUE      TRUE     
##          [,7]     
## result   0.6463022
## DID      266      
## chi_0.05 TRUE
Within Gilgit-Baltistan, 3 of 7 districts including Hunza has Chi > 0.05

GLM C003_01 ~ C002_01 and Anova for All Districts

DID_unique <- child_ica_dummy %>% summarize(unique(DID))
DID_unique <- as.matrix(DID_unique)
DID_unique <- as.numeric(DID_unique[,1])

dist_logist <- sapply(DID_unique, function(dist){
  
  glm_child_dist <- glm(C003_01 ~ C002_01, 
                        family = "binomial", 
                        data = child_ica_dummy %>% 
                          filter(DID == dist))
  
  glm_child_dist_null <- glm(C003_01~1, 
                             family = "binomial", 
                             data = child_ica_dummy %>% 
                               filter(DID == dist))

  anova <- anova(glm_child_dist_null, glm_child_dist, test = "Chisq")

data.frame(DID = dist, 
           chi = anova$`Pr(>Chi)`[2], 
           chi_largerThan_0.05 = anova$`Pr(>Chi)`[2] >= 0.05, 
           chi_largerThan_0.1 = anova$`Pr(>Chi)`[2] >= 0.1)
})

anova_dist <- as.data.frame(as.tibble(t(dist_logist)))
## Warning: `as.tibble()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
anova_dist$DID <- as.numeric(anova_dist$DID)
anova_dist$chi <- as.numeric(anova_dist$chi)
anova_dist$chi_largerThan_0.1 <- as.logical(anova_dist$chi_largerThan_0.1)
anova_dist$chi_largerThan_0.05 <- as.logical(anova_dist$chi_largerThan_0.05)
anova_dist %>% 
  summarize(total_chi_largerThan_0.05 = sum(as.numeric(chi_largerThan_0.05)),
            ratio_0.05 = sum(as.numeric(chi_largerThan_0.05))/length(chi_largerThan_0.05), 
            total_chi_largerThan_0.1 = sum(as.numeric(chi_largerThan_0.1)),
            ratio_0.1 = sum(as.numeric(chi_largerThan_0.1))/length(chi_largerThan_0.1))
DIDs with chi >= 0.05
anova_dist %>% 
  filter(chi_largerThan_0.05 == TRUE) %>% 
  select(-chi_largerThan_0.1)
DID chi > 0.05
DID_chi_0.05 <- anova_dist %>% 
  filter(chi_largerThan_0.05 == TRUE) %>% 
  select(DID) %>% 
  pull()
DID_chi_0.05
##  [1] 146 151 156 158 159 162 163 164 167 171 173 176 178 179 189 196 245 257 264
## [20] 265 266 267 268 269 270 271 272 273 274 276
DIDs with chi >= 0.1
anova_dist %>% 
  filter(chi_largerThan_0.1 == TRUE) %>% 
  select(-chi_largerThan_0.05)
DID chi > 0.1
DID_chi_0.1 <- anova_dist %>% 
  filter(chi_largerThan_0.1 == TRUE) %>% 
  select(DID) %>% 
  pull()
DID with C002 == -1, chi > 0.05
child %>% 
  filter(C002 == -1) %>% 
  group_by(DID) %>% 
  mutate(avg_learning = mean(C010, na.rm = TRUE)) %>% 
  ggplot(aes(DID, avg_learning)) +
  geom_point(data = child %>% 
               filter(C002 == -1 & DID == DID_chi_0.05) %>% 
               group_by(DID) %>%
               mutate(avg_learning = mean(C010, na.rm = TRUE)), size = 4, color = "red") +
  geom_point(aes(color = RID)) +
  geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE)
## Warning in DID == DID_chi_0.05: 長いオブジェクトの長さが短いオブジェクトの長さの
## 倍数になっていません

C001 & enroll ; DID chi > 0.1
child %>% 
  filter(DID == DID_chi_0.1) %>%
  group_by(DID, C001) %>% 
  mutate(enroll = mean(C003 == 3)) %>% 
  ggplot(aes(C001, enroll)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(DID~.)
## Warning in DID == DID_chi_0.1: 長いオブジェクトの長さが短いオブジェクトの長さの
## 倍数になっていません
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

GLM C003_01 ~ n_children_in_household + H002 + C002_01 + as.factor(DID)

glm_child <- glm(C003_01 ~ n_children_in_household + H002 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01 + 
##     as.factor(DID), family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5433  -1.0857   0.6008   0.8455   1.6963  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              1.856387   0.090069  20.611  < 2e-16 ***
## n_children_in_household -0.030048   0.003248  -9.252  < 2e-16 ***
## H002                     0.238646   0.007614  31.342  < 2e-16 ***
## C002_01                 -0.661321   0.009514 -69.509  < 2e-16 ***
## as.factor(DID)147       -0.531421   0.106816  -4.975 6.52e-07 ***
## as.factor(DID)148       -0.080366   0.111073  -0.724 0.469342    
## as.factor(DID)149       -0.729406   0.102725  -7.101 1.24e-12 ***
## as.factor(DID)150       -0.755588   0.101817  -7.421 1.16e-13 ***
## as.factor(DID)151       -0.188233   0.111184  -1.693 0.090458 .  
## as.factor(DID)152       -0.294774   0.115283  -2.557 0.010559 *  
## as.factor(DID)153       -0.876057   0.103921  -8.430  < 2e-16 ***
## as.factor(DID)154       -0.635280   0.105517  -6.021 1.74e-09 ***
## as.factor(DID)155       -0.824648   0.101025  -8.163 3.27e-16 ***
## as.factor(DID)156       -1.295679   0.115699 -11.199  < 2e-16 ***
## as.factor(DID)157       -0.457369   0.110809  -4.128 3.67e-05 ***
## as.factor(DID)158       -0.353198   0.107855  -3.275 0.001058 ** 
## as.factor(DID)159       -1.312255   0.106407 -12.332  < 2e-16 ***
## as.factor(DID)160       -0.699375   0.104766  -6.676 2.46e-11 ***
## as.factor(DID)161       -1.308519   0.097458 -13.426  < 2e-16 ***
## as.factor(DID)162        0.210991   0.125085   1.687 0.091645 .  
## as.factor(DID)163        0.071172   0.127802   0.557 0.577599    
## as.factor(DID)164       -0.508366   0.113661  -4.473 7.73e-06 ***
## as.factor(DID)165       -0.991673   0.104159  -9.521  < 2e-16 ***
## as.factor(DID)166       -0.869402   0.104533  -8.317  < 2e-16 ***
## as.factor(DID)167        0.526340   0.130238   4.041 5.31e-05 ***
## as.factor(DID)169       -1.655333   0.099225 -16.683  < 2e-16 ***
## as.factor(DID)170       -0.488745   0.108583  -4.501 6.76e-06 ***
## as.factor(DID)171       -0.436012   0.110567  -3.943 8.03e-05 ***
## as.factor(DID)172       -0.624901   0.106396  -5.873 4.27e-09 ***
## as.factor(DID)173       -0.306239   0.109770  -2.790 0.005274 ** 
## as.factor(DID)174       -0.895481   0.105491  -8.489  < 2e-16 ***
## as.factor(DID)175       -1.320879   0.101365 -13.031  < 2e-16 ***
## as.factor(DID)176       -0.464176   0.114670  -4.048 5.17e-05 ***
## as.factor(DID)177       -0.853066   0.102982  -8.284  < 2e-16 ***
## as.factor(DID)178        0.182187   0.117181   1.555 0.120006    
## as.factor(DID)179       -0.718075   0.105393  -6.813 9.54e-12 ***
## as.factor(DID)180       -0.673160   0.105975  -6.352 2.12e-10 ***
## as.factor(DID)181       -0.158811   0.115371  -1.377 0.168658    
## as.factor(DID)182       -1.252132   0.100397 -12.472  < 2e-16 ***
## as.factor(DID)183       -0.624218   0.107630  -5.800 6.64e-09 ***
## as.factor(DID)184       -1.279621   0.099900 -12.809  < 2e-16 ***
## as.factor(DID)185        0.315727   0.116482   2.711 0.006718 ** 
## as.factor(DID)186       -1.527947   0.099616 -15.338  < 2e-16 ***
## as.factor(DID)187       -1.365833   0.101106 -13.509  < 2e-16 ***
## as.factor(DID)188       -1.201238   0.100289 -11.978  < 2e-16 ***
## as.factor(DID)189       -0.279213   0.108191  -2.581 0.009858 ** 
## as.factor(DID)190       -0.984810   0.108473  -9.079  < 2e-16 ***
## as.factor(DID)191       -0.856359   0.108294  -7.908 2.62e-15 ***
## as.factor(DID)192       -0.907494   0.102627  -8.843  < 2e-16 ***
## as.factor(DID)193       -0.062637   0.110415  -0.567 0.570519    
## as.factor(DID)194       -1.641845   0.099810 -16.450  < 2e-16 ***
## as.factor(DID)195       -1.800501   0.100992 -17.828  < 2e-16 ***
## as.factor(DID)196       -0.643909   0.109777  -5.866 4.47e-09 ***
## as.factor(DID)197       -1.333062   0.100752 -13.231  < 2e-16 ***
## as.factor(DID)198       -1.750612   0.099615 -17.574  < 2e-16 ***
## as.factor(DID)199        0.411202   0.116763   3.522 0.000429 ***
## as.factor(DID)200       -1.504418   0.102715 -14.647  < 2e-16 ***
## as.factor(DID)202       -1.430222   0.100851 -14.182  < 2e-16 ***
## as.factor(DID)203       -1.244656   0.099211 -12.546  < 2e-16 ***
## as.factor(DID)204       -0.864444   0.103348  -8.364  < 2e-16 ***
## as.factor(DID)205       -1.475565   0.098277 -15.014  < 2e-16 ***
## as.factor(DID)206       -1.502885   0.098520 -15.255  < 2e-16 ***
## as.factor(DID)207       -1.931182   0.100015 -19.309  < 2e-16 ***
## as.factor(DID)208       -1.433055   0.099404 -14.417  < 2e-16 ***
## as.factor(DID)209       -0.835136   0.108254  -7.715 1.21e-14 ***
## as.factor(DID)210       -1.656130   0.097887 -16.919  < 2e-16 ***
## as.factor(DID)211       -0.940851   0.107889  -8.721  < 2e-16 ***
## as.factor(DID)212       -1.184811   0.102333 -11.578  < 2e-16 ***
## as.factor(DID)213       -1.492092   0.098622 -15.129  < 2e-16 ***
## as.factor(DID)214       -1.980250   0.099212 -19.960  < 2e-16 ***
## as.factor(DID)215       -1.277002   0.096980 -13.168  < 2e-16 ***
## as.factor(DID)216       -1.475047   0.101753 -14.496  < 2e-16 ***
## as.factor(DID)217       -1.645835   0.099970 -16.463  < 2e-16 ***
## as.factor(DID)218       -1.391871   0.097592 -14.262  < 2e-16 ***
## as.factor(DID)219       -1.414559   0.100711 -14.046  < 2e-16 ***
## as.factor(DID)220       -2.067994   0.096336 -21.466  < 2e-16 ***
## as.factor(DID)221       -2.314209   0.101510 -22.798  < 2e-16 ***
## as.factor(DID)222       -1.569588   0.097845 -16.042  < 2e-16 ***
## as.factor(DID)223       -1.497175   0.105467 -14.196  < 2e-16 ***
## as.factor(DID)224       -2.024431   0.099120 -20.424  < 2e-16 ***
## as.factor(DID)225       -1.626735   0.099947 -16.276  < 2e-16 ***
## as.factor(DID)226       -1.380602   0.102822 -13.427  < 2e-16 ***
## as.factor(DID)227       -1.587866   0.098118 -16.183  < 2e-16 ***
## as.factor(DID)228       -2.149597   0.103253 -20.819  < 2e-16 ***
## as.factor(DID)229       -2.016092   0.101742 -19.816  < 2e-16 ***
## as.factor(DID)230       -1.952694   0.099633 -19.599  < 2e-16 ***
## as.factor(DID)231       -1.323551   0.098863 -13.388  < 2e-16 ***
## as.factor(DID)232       -0.149362   0.107653  -1.387 0.165307    
## as.factor(DID)233       -2.172729   0.104344 -20.823  < 2e-16 ***
## as.factor(DID)234       -1.481327   0.105267 -14.072  < 2e-16 ***
## as.factor(DID)235       -0.728578   0.102880  -7.082 1.42e-12 ***
## as.factor(DID)236       -1.011111   0.103059  -9.811  < 2e-16 ***
## as.factor(DID)237       -0.768246   0.105928  -7.253 4.09e-13 ***
## as.factor(DID)238        0.092540   0.116242   0.796 0.425973    
## as.factor(DID)239       -0.895697   0.102414  -8.746  < 2e-16 ***
## as.factor(DID)240       -1.139789   0.102174 -11.155  < 2e-16 ***
## as.factor(DID)241       -2.331117   0.098219 -23.734  < 2e-16 ***
## as.factor(DID)242       -1.090313   0.103367 -10.548  < 2e-16 ***
## as.factor(DID)243       -1.092509   0.108519 -10.067  < 2e-16 ***
## as.factor(DID)244       -0.389501   0.110870  -3.513 0.000443 ***
## as.factor(DID)245        0.307911   0.125277   2.458 0.013978 *  
## as.factor(DID)246       -1.693232   0.100045 -16.925  < 2e-16 ***
## as.factor(DID)247       -1.233913   0.107645 -11.463  < 2e-16 ***
## as.factor(DID)248       -0.789366   0.100028  -7.891 2.99e-15 ***
## as.factor(DID)249       -0.023336   0.124757  -0.187 0.851620    
## as.factor(DID)250       -0.687107   0.105155  -6.534 6.39e-11 ***
## as.factor(DID)251       -0.540857   0.106874  -5.061 4.18e-07 ***
## as.factor(DID)252       -0.860171   0.103757  -8.290  < 2e-16 ***
## as.factor(DID)253       -0.422480   0.111991  -3.772 0.000162 ***
## as.factor(DID)254       -1.716154   0.115165 -14.902  < 2e-16 ***
## as.factor(DID)255       -0.718627   0.108954  -6.596 4.23e-11 ***
## as.factor(DID)256       -0.192197   0.106990  -1.796 0.072430 .  
## as.factor(DID)257        0.525470   0.140459   3.741 0.000183 ***
## as.factor(DID)258       -1.393632   0.099084 -14.065  < 2e-16 ***
## as.factor(DID)259       -0.803014   0.107179  -7.492 6.77e-14 ***
## as.factor(DID)260       -0.030875   0.108454  -0.285 0.775887    
## as.factor(DID)261       -2.203777   0.098347 -22.408  < 2e-16 ***
## as.factor(DID)262       -0.562816   0.103736  -5.425 5.78e-08 ***
## as.factor(DID)263       -0.168283   0.107682  -1.563 0.118105    
## as.factor(DID)264       -0.378301   0.105640  -3.581 0.000342 ***
## as.factor(DID)265        0.045178   0.114401   0.395 0.692909    
## as.factor(DID)266        0.651750   0.130083   5.010 5.44e-07 ***
## as.factor(DID)267       -0.138255   0.106774  -1.295 0.195374    
## as.factor(DID)268       -0.998930   0.104179  -9.589  < 2e-16 ***
## as.factor(DID)269        0.089197   0.112064   0.796 0.426058    
## as.factor(DID)270        0.047179   0.111679   0.422 0.672695    
## as.factor(DID)271        0.316533   0.115617   2.738 0.006186 ** 
## as.factor(DID)272        0.145384   0.112480   1.293 0.196175    
## as.factor(DID)273       -0.121213   0.109283  -1.109 0.267356    
## as.factor(DID)274        0.006667   0.120782   0.055 0.955983    
## as.factor(DID)275       -1.031188   0.101880 -10.122  < 2e-16 ***
## as.factor(DID)276       -0.061414   0.106435  -0.577 0.563931    
## as.factor(DID)277       -0.253596   0.146904  -1.726 0.084300 .  
## as.factor(DID)278       -0.665286   0.098548  -6.751 1.47e-11 ***
## as.factor(DID)279       -1.754758   0.100067 -17.536  < 2e-16 ***
## as.factor(DID)280       -0.906296   0.102311  -8.858  < 2e-16 ***
## as.factor(DID)281       -0.667260   0.101980  -6.543 6.03e-11 ***
## as.factor(DID)282       -0.588057   0.109290  -5.381 7.42e-08 ***
## as.factor(DID)284       -0.676702   0.104745  -6.460 1.04e-10 ***
## as.factor(DID)287       -0.907293   0.104730  -8.663  < 2e-16 ***
## as.factor(DID)289       -0.917523   0.101097  -9.076  < 2e-16 ***
## as.factor(DID)290       -0.888790   0.104415  -8.512  < 2e-16 ***
## as.factor(DID)315       -0.736484   0.115424  -6.381 1.76e-10 ***
## as.factor(DID)316       -1.195621   0.103364 -11.567  < 2e-16 ***
## as.factor(DID)318       -1.593066   0.105466 -15.105  < 2e-16 ***
## as.factor(DID)319       -2.132163   0.099025 -21.532  < 2e-16 ***
## as.factor(DID)320       -1.398009   0.104773 -13.343  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 269755  on 245600  degrees of freedom
## AIC: 270049
## 
## Number of Fisher Scoring iterations: 5
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 330.28, df = 8, p-value < 2.2e-16

GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)

glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5059  -1.0668   0.6014   0.8352   1.7174  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              2.161512   0.088576  24.403  < 2e-16 ***
## n_children_in_household -0.028508   0.003251  -8.768  < 2e-16 ***
## PR004_PR009_01           0.347071   0.010500  33.054  < 2e-16 ***
## C002_01                 -0.664193   0.009519 -69.776  < 2e-16 ***
## as.factor(DID)147       -0.607598   0.106738  -5.692 1.25e-08 ***
## as.factor(DID)148       -0.093994   0.111048  -0.846 0.397314    
## as.factor(DID)149       -0.804572   0.102678  -7.836 4.65e-15 ***
## as.factor(DID)150       -0.784029   0.101828  -7.700 1.37e-14 ***
## as.factor(DID)151       -0.190780   0.111171  -1.716 0.086146 .  
## as.factor(DID)152       -0.334471   0.115245  -2.902 0.003705 ** 
## as.factor(DID)153       -0.900790   0.103870  -8.672  < 2e-16 ***
## as.factor(DID)154       -0.678256   0.105494  -6.429 1.28e-10 ***
## as.factor(DID)155       -0.842393   0.101011  -8.340  < 2e-16 ***
## as.factor(DID)156       -1.313467   0.115592 -11.363  < 2e-16 ***
## as.factor(DID)157       -0.452119   0.110799  -4.081 4.49e-05 ***
## as.factor(DID)158       -0.343909   0.107871  -3.188 0.001432 ** 
## as.factor(DID)159       -1.309531   0.106342 -12.314  < 2e-16 ***
## as.factor(DID)160       -0.738367   0.104702  -7.052 1.76e-12 ***
## as.factor(DID)161       -1.277624   0.097450 -13.111  < 2e-16 ***
## as.factor(DID)162        0.140673   0.125046   1.125 0.260600    
## as.factor(DID)163        0.076054   0.127784   0.595 0.551728    
## as.factor(DID)164       -0.566415   0.113648  -4.984 6.23e-07 ***
## as.factor(DID)165       -0.978953   0.104104  -9.404  < 2e-16 ***
## as.factor(DID)166       -0.830884   0.104540  -7.948 1.90e-15 ***
## as.factor(DID)167        0.509703   0.130242   3.914 9.10e-05 ***
## as.factor(DID)169       -1.723715   0.099078 -17.398  < 2e-16 ***
## as.factor(DID)170       -0.472852   0.108538  -4.357 1.32e-05 ***
## as.factor(DID)171       -0.437550   0.110541  -3.958 7.55e-05 ***
## as.factor(DID)172       -0.638525   0.106358  -6.004 1.93e-09 ***
## as.factor(DID)173       -0.267159   0.109787  -2.433 0.014957 *  
## as.factor(DID)174       -0.956716   0.105408  -9.076  < 2e-16 ***
## as.factor(DID)175       -1.331763   0.101311 -13.145  < 2e-16 ***
## as.factor(DID)176       -0.472402   0.114611  -4.122 3.76e-05 ***
## as.factor(DID)177       -0.852129   0.102954  -8.277  < 2e-16 ***
## as.factor(DID)178        0.202014   0.117141   1.725 0.084612 .  
## as.factor(DID)179       -0.718205   0.105390  -6.815 9.44e-12 ***
## as.factor(DID)180       -0.678592   0.105962  -6.404 1.51e-10 ***
## as.factor(DID)181       -0.117571   0.115344  -1.019 0.308054    
## as.factor(DID)182       -1.388829   0.100242 -13.855  < 2e-16 ***
## as.factor(DID)183       -0.659636   0.107584  -6.131 8.71e-10 ***
## as.factor(DID)184       -1.307346   0.099855 -13.092  < 2e-16 ***
## as.factor(DID)185        0.289557   0.116541   2.485 0.012970 *  
## as.factor(DID)186       -1.693746   0.099333 -17.051  < 2e-16 ***
## as.factor(DID)187       -1.457246   0.100967 -14.433  < 2e-16 ***
## as.factor(DID)188       -1.301699   0.100182 -12.993  < 2e-16 ***
## as.factor(DID)189       -0.357912   0.108160  -3.309 0.000936 ***
## as.factor(DID)190       -0.994167   0.108433  -9.168  < 2e-16 ***
## as.factor(DID)191       -0.905377   0.108243  -8.364  < 2e-16 ***
## as.factor(DID)192       -1.059675   0.102504 -10.338  < 2e-16 ***
## as.factor(DID)193       -0.060710   0.110471  -0.550 0.582622    
## as.factor(DID)194       -1.683985   0.099732 -16.885  < 2e-16 ***
## as.factor(DID)195       -1.882723   0.100866 -18.666  < 2e-16 ***
## as.factor(DID)196       -0.698907   0.109777  -6.367 1.93e-10 ***
## as.factor(DID)197       -1.429311   0.100649 -14.201  < 2e-16 ***
## as.factor(DID)198       -1.785280   0.099553 -17.933  < 2e-16 ***
## as.factor(DID)199        0.351822   0.116662   3.016 0.002564 ** 
## as.factor(DID)200       -1.457248   0.102727 -14.186  < 2e-16 ***
## as.factor(DID)202       -1.476437   0.100767 -14.652  < 2e-16 ***
## as.factor(DID)203       -1.442589   0.099023 -14.568  < 2e-16 ***
## as.factor(DID)204       -0.894943   0.103239  -8.669  < 2e-16 ***
## as.factor(DID)205       -1.587176   0.098221 -16.159  < 2e-16 ***
## as.factor(DID)206       -1.652157   0.098176 -16.828  < 2e-16 ***
## as.factor(DID)207       -2.029097   0.099744 -20.343  < 2e-16 ***
## as.factor(DID)208       -1.513908   0.099299 -15.246  < 2e-16 ***
## as.factor(DID)209       -1.027678   0.107955  -9.519  < 2e-16 ***
## as.factor(DID)210       -1.723325   0.097692 -17.640  < 2e-16 ***
## as.factor(DID)211       -1.029961   0.107737  -9.560  < 2e-16 ***
## as.factor(DID)212       -1.344713   0.102072 -13.174  < 2e-16 ***
## as.factor(DID)213       -1.582224   0.098485 -16.066  < 2e-16 ***
## as.factor(DID)214       -2.095527   0.099005 -21.166  < 2e-16 ***
## as.factor(DID)215       -1.359969   0.096766 -14.054  < 2e-16 ***
## as.factor(DID)216       -1.675878   0.101392 -16.529  < 2e-16 ***
## as.factor(DID)217       -1.709895   0.099764 -17.139  < 2e-16 ***
## as.factor(DID)218       -1.634273   0.097160 -16.820  < 2e-16 ***
## as.factor(DID)219       -1.542831   0.100494 -15.352  < 2e-16 ***
## as.factor(DID)220       -2.103022   0.096242 -21.851  < 2e-16 ***
## as.factor(DID)221       -2.455716   0.101216 -24.262  < 2e-16 ***
## as.factor(DID)222       -1.692441   0.097663 -17.329  < 2e-16 ***
## as.factor(DID)223       -1.604057   0.105314 -15.231  < 2e-16 ***
## as.factor(DID)224       -2.148013   0.098930 -21.712  < 2e-16 ***
## as.factor(DID)225       -1.852791   0.099548 -18.612  < 2e-16 ***
## as.factor(DID)226       -1.388128   0.102732 -13.512  < 2e-16 ***
## as.factor(DID)227       -1.677606   0.097855 -17.144  < 2e-16 ***
## as.factor(DID)228       -2.257037   0.102988 -21.916  < 2e-16 ***
## as.factor(DID)229       -1.971458   0.101725 -19.380  < 2e-16 ***
## as.factor(DID)230       -2.137518   0.099303 -21.525  < 2e-16 ***
## as.factor(DID)231       -1.403955   0.098699 -14.225  < 2e-16 ***
## as.factor(DID)232       -0.293014   0.107407  -2.728 0.006370 ** 
## as.factor(DID)233       -2.270591   0.104146 -21.802  < 2e-16 ***
## as.factor(DID)234       -1.583978   0.105021 -15.082  < 2e-16 ***
## as.factor(DID)235       -0.832266   0.102738  -8.101 5.46e-16 ***
## as.factor(DID)236       -1.105162   0.102937 -10.736  < 2e-16 ***
## as.factor(DID)237       -0.764733   0.105896  -7.222 5.14e-13 ***
## as.factor(DID)238        0.248673   0.116251   2.139 0.032427 *  
## as.factor(DID)239       -0.895892   0.102392  -8.750  < 2e-16 ***
## as.factor(DID)240       -1.242176   0.102128 -12.163  < 2e-16 ***
## as.factor(DID)241       -2.369040   0.098180 -24.129  < 2e-16 ***
## as.factor(DID)242       -1.101073   0.103317 -10.657  < 2e-16 ***
## as.factor(DID)243       -1.057710   0.108492  -9.749  < 2e-16 ***
## as.factor(DID)244       -0.560656   0.110773  -5.061 4.16e-07 ***
## as.factor(DID)245        0.261619   0.125199   2.090 0.036652 *  
## as.factor(DID)246       -1.706840   0.100013 -17.066  < 2e-16 ***
## as.factor(DID)247       -1.234545   0.107606 -11.473  < 2e-16 ***
## as.factor(DID)248       -0.945059   0.099728  -9.476  < 2e-16 ***
## as.factor(DID)249        0.026476   0.124731   0.212 0.831901    
## as.factor(DID)250       -0.721227   0.105067  -6.864 6.68e-12 ***
## as.factor(DID)251       -0.688719   0.106677  -6.456 1.07e-10 ***
## as.factor(DID)252       -0.913442   0.103706  -8.808  < 2e-16 ***
## as.factor(DID)253       -0.511879   0.111901  -4.574 4.78e-06 ***
## as.factor(DID)254       -1.762981   0.115162 -15.309  < 2e-16 ***
## as.factor(DID)255       -0.856150   0.108863  -7.864 3.71e-15 ***
## as.factor(DID)256       -0.251852   0.106913  -2.356 0.018489 *  
## as.factor(DID)257        0.505590   0.140392   3.601 0.000317 ***
## as.factor(DID)258       -1.562184   0.098968 -15.785  < 2e-16 ***
## as.factor(DID)259       -0.846290   0.107113  -7.901 2.77e-15 ***
## as.factor(DID)260       -0.132057   0.108378  -1.218 0.223037    
## as.factor(DID)261       -2.301424   0.098132 -23.452  < 2e-16 ***
## as.factor(DID)262       -0.725316   0.103528  -7.006 2.45e-12 ***
## as.factor(DID)263       -0.414257   0.107373  -3.858 0.000114 ***
## as.factor(DID)264       -0.444065   0.105615  -4.205 2.62e-05 ***
## as.factor(DID)265       -0.075139   0.114318  -0.657 0.510998    
## as.factor(DID)266        0.615342   0.130073   4.731 2.24e-06 ***
## as.factor(DID)267       -0.257368   0.106773  -2.410 0.015934 *  
## as.factor(DID)268       -1.115655   0.104103 -10.717  < 2e-16 ***
## as.factor(DID)269       -0.143548   0.111944  -1.282 0.199729    
## as.factor(DID)270       -0.052250   0.111599  -0.468 0.639648    
## as.factor(DID)271        0.056815   0.115452   0.492 0.622643    
## as.factor(DID)272       -0.088445   0.112363  -0.787 0.431202    
## as.factor(DID)273       -0.338363   0.109176  -3.099 0.001940 ** 
## as.factor(DID)274       -0.169419   0.120699  -1.404 0.160423    
## as.factor(DID)275       -1.110405   0.101896 -10.897  < 2e-16 ***
## as.factor(DID)276       -0.187979   0.106426  -1.766 0.077348 .  
## as.factor(DID)277       -0.200188   0.146869  -1.363 0.172873    
## as.factor(DID)278       -0.791947   0.098415  -8.047 8.49e-16 ***
## as.factor(DID)279       -1.831521   0.099957 -18.323  < 2e-16 ***
## as.factor(DID)280       -1.045823   0.102210 -10.232  < 2e-16 ***
## as.factor(DID)281       -0.817280   0.101859  -8.024 1.03e-15 ***
## as.factor(DID)282       -0.777911   0.109042  -7.134 9.75e-13 ***
## as.factor(DID)284       -0.782806   0.104522  -7.489 6.92e-14 ***
## as.factor(DID)287       -1.035915   0.104583  -9.905  < 2e-16 ***
## as.factor(DID)289       -0.883532   0.101090  -8.740  < 2e-16 ***
## as.factor(DID)290       -1.049015   0.104241 -10.063  < 2e-16 ***
## as.factor(DID)315       -0.730629   0.115409  -6.331 2.44e-10 ***
## as.factor(DID)316       -1.090704   0.103373 -10.551  < 2e-16 ***
## as.factor(DID)318       -1.666857   0.105254 -15.837  < 2e-16 ***
## as.factor(DID)319       -2.273787   0.098779 -23.019  < 2e-16 ***
## as.factor(DID)320       -1.525834   0.104652 -14.580  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 269656  on 245600  degrees of freedom
## AIC: 269950
## 
## Number of Fisher Scoring iterations: 5
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 415.55, df = 8, p-value < 2.2e-16
exponential transformation
exp(glm_child$coefficients)
##             (Intercept) n_children_in_household          PR004_PR009_01 
##              8.68426215              0.97189458              1.41491756 
##                 C002_01       as.factor(DID)147       as.factor(DID)148 
##              0.51468855              0.54465757              0.91028798 
##       as.factor(DID)149       as.factor(DID)150       as.factor(DID)151 
##              0.44727950              0.45656288              0.82631471 
##       as.factor(DID)152       as.factor(DID)153       as.factor(DID)154 
##              0.71571648              0.40624870              0.50750152 
##       as.factor(DID)155       as.factor(DID)156       as.factor(DID)157 
##              0.43067852              0.26888624              0.63627844 
##       as.factor(DID)158       as.factor(DID)159       as.factor(DID)160 
##              0.70899315              0.26994651              0.47789348 
##       as.factor(DID)161       as.factor(DID)162       as.factor(DID)163 
##              0.27869861              1.15104847              1.07902058 
##       as.factor(DID)164       as.factor(DID)165       as.factor(DID)166 
##              0.56755636              0.37570439              0.43566418 
##       as.factor(DID)167       as.factor(DID)169       as.factor(DID)170 
##              1.66479663              0.17840212              0.62322241 
##       as.factor(DID)171       as.factor(DID)172       as.factor(DID)173 
##              0.64561636              0.52807089              0.76555125 
##       as.factor(DID)174       as.factor(DID)175       as.factor(DID)176 
##              0.38415219              0.26401140              0.62350291 
##       as.factor(DID)177       as.factor(DID)178       as.factor(DID)179 
##              0.42650577              1.22386457              0.48762652 
##       as.factor(DID)180       as.factor(DID)181       as.factor(DID)182 
##              0.50733088              0.88907702              0.24936704 
##       as.factor(DID)183       as.factor(DID)184       as.factor(DID)185 
##              0.51703930              0.27053703              1.33583600 
##       as.factor(DID)186       as.factor(DID)187       as.factor(DID)188 
##              0.18382953              0.23287662              0.27206914 
##       as.factor(DID)189       as.factor(DID)190       as.factor(DID)191 
##              0.69913479              0.37003145              0.40438937 
##       as.factor(DID)192       as.factor(DID)193       as.factor(DID)194 
##              0.34656849              0.94109585              0.18563282 
##       as.factor(DID)195       as.factor(DID)196       as.factor(DID)197 
##              0.15217520              0.49712841              0.23947388 
##       as.factor(DID)198       as.factor(DID)199       as.factor(DID)200 
##              0.16775016              1.42165604              0.23287626 
##       as.factor(DID)202       as.factor(DID)203       as.factor(DID)204 
##              0.22845019              0.23631521              0.40863100 
##       as.factor(DID)205       as.factor(DID)206       as.factor(DID)207 
##              0.20450233              0.19163614              0.13145413 
##       as.factor(DID)208       as.factor(DID)209       as.factor(DID)210 
##              0.22004826              0.35783680              0.17847180 
##       as.factor(DID)211       as.factor(DID)212       as.factor(DID)213 
##              0.35702078              0.26061459              0.20551759 
##       as.factor(DID)214       as.factor(DID)215       as.factor(DID)216 
##              0.12300538              0.25666877              0.18714385 
##       as.factor(DID)217       as.factor(DID)218       as.factor(DID)219 
##              0.18088477              0.19509408              0.21377515 
##       as.factor(DID)220       as.factor(DID)221       as.factor(DID)222 
##              0.12208693              0.08580172              0.18406963 
##       as.factor(DID)223       as.factor(DID)224       as.factor(DID)225 
##              0.20107914              0.11671582              0.15679900 
##       as.factor(DID)226       as.factor(DID)227       as.factor(DID)228 
##              0.24954195              0.18682063              0.10466013 
##       as.factor(DID)229       as.factor(DID)230       as.factor(DID)231 
##              0.13925361              0.11794719              0.24562354 
##       as.factor(DID)232       as.factor(DID)233       as.factor(DID)234 
##              0.74601182              0.10325112              0.20515743 
##       as.factor(DID)235       as.factor(DID)236       as.factor(DID)237 
##              0.43506233              0.33115733              0.46545817 
##       as.factor(DID)238       as.factor(DID)239       as.factor(DID)240 
##              1.28232283              0.40824317              0.28875517 
##       as.factor(DID)241       as.factor(DID)242       as.factor(DID)243 
##              0.09357048              0.33251418              0.34725018 
##       as.factor(DID)244       as.factor(DID)245       as.factor(DID)246 
##              0.57083429              1.29903108              0.18143825 
##       as.factor(DID)247       as.factor(DID)248       as.factor(DID)249 
##              0.29096723              0.38865667              1.02682949 
##       as.factor(DID)250       as.factor(DID)251       as.factor(DID)252 
##              0.48615558              0.50221916              0.40114117 
##       as.factor(DID)253       as.factor(DID)254       as.factor(DID)255 
##              0.59936809              0.17153272              0.42479420 
##       as.factor(DID)256       as.factor(DID)257       as.factor(DID)258 
##              0.77736012              1.65796325              0.20967757 
##       as.factor(DID)259       as.factor(DID)260       as.factor(DID)261 
##              0.42900378              0.87629061              0.10011614 
##       as.factor(DID)262       as.factor(DID)263       as.factor(DID)264 
##              0.48417163              0.66083096              0.64142358 
##       as.factor(DID)265       as.factor(DID)266       as.factor(DID)267 
##              0.92761446              1.85028857              0.77308363 
##       as.factor(DID)268       as.factor(DID)269       as.factor(DID)270 
##              0.32770047              0.86627896              0.94909197 
##       as.factor(DID)271       as.factor(DID)272       as.factor(DID)273 
##              1.05845992              0.91535372              0.71293627 
##       as.factor(DID)274       as.factor(DID)275       as.factor(DID)276 
##              0.84415517              0.32942564              0.82863226 
##       as.factor(DID)277       as.factor(DID)278       as.factor(DID)279 
##              0.81857708              0.45296190              0.16016976 
##       as.factor(DID)280       as.factor(DID)281       as.factor(DID)282 
##              0.35140253              0.44163131              0.45936450 
##       as.factor(DID)284       as.factor(DID)287       as.factor(DID)289 
##              0.45712131              0.35490148              0.41332046 
##       as.factor(DID)290       as.factor(DID)315       as.factor(DID)316 
##              0.35028265              0.48160583              0.33598000 
##       as.factor(DID)318       as.factor(DID)319       as.factor(DID)320 
##              0.18883967              0.10292172              0.21743960
confidence interval (intercept and coefficient)
# confint(glm_child, level = 0.95)
exponential transformation of confidence interval (odds ratio of intercept and coefficient)
# exp(confint(glm_child, level = 0.95))
AIC
extractAIC(glm_child)
## [1]    147.0 269950.4
BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))
## [1]    147 271481
effectiveness of explanatory variables
glm_child_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy)
anova(glm_child_null, glm_child, test = "Chisq")
variables selection
# step(glm_child_null, direction = "both", 
#      scope = (~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)))
multicolinearity
vif(glm_child)
##                             GVIF  Df GVIF^(1/(2*Df))
## n_children_in_household 1.146849   1        1.070910
## PR004_PR009_01          1.246852   1        1.116625
## C002_01                 1.024292   1        1.012073
## as.factor(DID)          1.441880 143        1.001280
Districts with P-Values Bigger Than 0.05 and Coefficient
glm_summary <- glm_child %>% summary()
glm_coef <- as.data.frame(glm_summary$coefficients)
glm_coef %>% filter(`Pr(>|z|)` > 0.05)
Districts with P-Values Bigger Than 0.05
glm_coef %>% filter(`Pr(>|z|)` > 0.05) %>% t() %>% .[-c(1:5),]
##      as.factor(DID)148 as.factor(DID)151 as.factor(DID)162 as.factor(DID)163
##      as.factor(DID)178 as.factor(DID)181 as.factor(DID)193 as.factor(DID)249
##      as.factor(DID)260 as.factor(DID)265 as.factor(DID)269 as.factor(DID)270
##      as.factor(DID)271 as.factor(DID)272 as.factor(DID)274 as.factor(DID)276
##      as.factor(DID)277
dists_not_fit <-  c(148, 151, 162, 163, 178, 181, 193, 249, 260, 265, 269, 270, 271, 272, 274, 276, 277)
child_ica_dummy %>% filter(DID == dists_not_fit) %>% select(DID, DNAME) %>% summarize(DID = unique(DID), DNAME = unique(DNAME))
## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません
Districts with P-Values Bigger Than 0.001 and Coefficient
glm_summary <- glm_child %>% summary()
glm_coef <- as.data.frame(glm_summary$coefficients)
glm_coef %>% filter(`Pr(>|z|)` > 0.001)
Districts with P-Values Bigger Than 0.001
glm_coef %>% filter(`Pr(>|z|)` > 0.001) %>% t() %>% .[-c(1:5),]
##      as.factor(DID)148 as.factor(DID)151 as.factor(DID)152 as.factor(DID)158
##      as.factor(DID)162 as.factor(DID)163 as.factor(DID)173 as.factor(DID)178
##      as.factor(DID)181 as.factor(DID)185 as.factor(DID)193 as.factor(DID)199
##      as.factor(DID)232 as.factor(DID)238 as.factor(DID)245 as.factor(DID)249
##      as.factor(DID)256 as.factor(DID)260 as.factor(DID)265 as.factor(DID)267
##      as.factor(DID)269 as.factor(DID)270 as.factor(DID)271 as.factor(DID)272
##      as.factor(DID)273 as.factor(DID)274 as.factor(DID)276 as.factor(DID)277
dists_not_fit <-  c(148, 151, 152, 158, 162, 163, 173, 178, 181, 185, 193, 199, 232, 238, 245, 249, 256, 260, 265, 267, 269, 270, 271, 272, 273, 274, 276, 277)
child_ica_dummy %>% filter(DID == dists_not_fit) %>% select(DID, DNAME) %>% summarize(DID = unique(DID), DNAME = unique(DNAME))
## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません

Dists_not_fit. GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01

All of dists_not_fit
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(VID), family = "binomial", data = child_ica_dummy %>% filter(DID %in% dists_not_fit))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     C002_01 + as.factor(VID), family = "binomial", data = child_ica_dummy %>% 
##     filter(DID %in% dists_not_fit))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.9975   0.2123   0.4372   0.5768   1.4738  
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)               1.702857   0.485396   3.508 0.000451 ***
## n_children_in_household   0.036001   0.012295   2.928 0.003410 ** 
## PR004_PR009_01            0.085176   0.040443   2.106 0.035197 *  
## C002_01                  -0.181653   0.028957  -6.273 3.54e-10 ***
## as.factor(VID)4937       -0.475516   0.669974  -0.710 0.477857    
## as.factor(VID)4938        1.793771   1.123676   1.596 0.110412    
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## as.factor(VID)78263       2.338700   1.117720   2.092 0.036404 *  
## as.factor(VID)78266       0.087066   0.602409   0.145 0.885083    
## as.factor(VID)78267       0.842959   0.769826   1.095 0.273517    
## as.factor(VID)78268      -1.412754   0.545757  -2.589 0.009636 ** 
## as.factor(VID)78269      -1.541264   0.542124  -2.843 0.004469 ** 
## as.factor(VID)78270       2.481996   1.117535   2.221 0.026354 *  
## as.factor(VID)78271      15.821523 486.556290   0.033 0.974059    
## as.factor(VID)78272       2.299845   1.119029   2.055 0.039858 *  
## as.factor(VID)78273       1.385150   0.762101   1.818 0.069134 .  
## as.factor(VID)78276       1.780017   0.865352   2.057 0.039688 *  
## as.factor(VID)78277      -0.338993   0.598968  -0.566 0.571420    
## as.factor(VID)78279       0.273566   0.599968   0.456 0.648413    
## as.factor(VID)78283       0.214436   0.589283   0.364 0.715938    
## as.factor(VID)78285      15.803299 490.303674   0.032 0.974287    
## as.factor(VID)78286      15.791458 447.561366   0.035 0.971854    
## as.factor(VID)78288       2.346093   1.118685   2.097 0.035977 *  
## as.factor(VID)78291       0.140548   0.601302   0.234 0.815188    
## as.factor(VID)78292      15.795047 625.050119   0.025 0.979840    
## as.factor(VID)78295       2.389088   1.118482   2.136 0.032679 *  
## as.factor(VID)78298      15.761151 510.187737   0.031 0.975355    
## as.factor(VID)78300       1.620474   0.866619   1.870 0.061500 .  
## as.factor(VID)78301       1.554667   0.866750   1.794 0.072865 .  
## as.factor(VID)78302      -0.927305   0.558781  -1.660 0.097012 .  
## as.factor(VID)78303       2.248605   1.119261   2.009 0.044536 *  
## as.factor(VID)78309       0.186767   0.722130   0.259 0.795918    
## as.factor(VID)78312       0.642393   0.671884   0.956 0.339018    
## as.factor(VID)78313      15.753804 514.346726   0.031 0.975566    
## as.factor(VID)78542      -0.711497   0.575134  -1.237 0.216051    
## as.factor(VID)78592       0.892926   0.707791   1.262 0.207105    
## as.factor(VID)78593       0.258996   0.599295   0.432 0.665619    
## as.factor(VID)78594       1.655915   0.866624   1.911 0.056035 .  
## as.factor(VID)78595       0.600128   0.645188   0.930 0.352289    
## as.factor(VID)78596      -0.057576   0.584194  -0.099 0.921491    
## as.factor(VID)78597       0.272549   0.628454   0.434 0.664520    
## as.factor(VID)78598       1.237141   0.764049   1.619 0.105406    
## as.factor(VID)78599       0.992409   0.707280   1.403 0.160576    
## as.factor(VID)78600      -0.119587   0.659266  -0.181 0.856058    
## as.factor(VID)78601       0.618837   0.624635   0.991 0.321823    
## as.factor(VID)78602      -0.117786   0.619920  -0.190 0.849307    
## as.factor(VID)78604      -0.026189   0.633961  -0.041 0.967049    
## as.factor(VID)78605      -1.527879   0.537106  -2.845 0.004446 ** 
## as.factor(VID)78606       0.260018   0.599983   0.433 0.664743    
## as.factor(VID)78607       1.617264   0.866599   1.866 0.062011 .  
## as.factor(VID)78608      -1.831664   0.578401  -3.167 0.001541 ** 
## as.factor(VID)78609       0.037324   0.582709   0.064 0.948929    
## as.factor(VID)78611      -0.132736   0.570417  -0.233 0.815994    
## as.factor(VID)78613       0.032167   0.603057   0.053 0.957461    
## as.factor(VID)78614       1.646500   1.126585   1.461 0.143879    
## as.factor(VID)78616      -1.151708   0.588214  -1.958 0.050233 .  
## as.factor(VID)78617       0.808014   0.710055   1.138 0.255137    
## as.factor(VID)78619       0.939569   0.767507   1.224 0.220883    
## as.factor(VID)78638       0.743653   0.710549   1.047 0.295289    
## as.factor(VID)78643       0.084692   0.581771   0.146 0.884256    
## as.factor(VID)78649      -1.113646   0.546270  -2.039 0.041486 *  
## as.factor(VID)78650       1.422021   0.868324   1.638 0.101492    
## as.factor(VID)78651       1.302766   0.703869   1.851 0.064189 .  
## as.factor(VID)78652      -0.598458   0.566787  -1.056 0.291024    
## as.factor(VID)78654      -0.366908   0.609070  -0.602 0.546904    
## as.factor(VID)78683       2.355678   1.117978   2.107 0.035110 *  
## as.factor(VID)78687       0.833156   0.708954   1.175 0.239919    
## as.factor(VID)78689       0.168448   0.613980   0.274 0.783812    
## as.factor(VID)78691       0.708219   0.671415   1.055 0.291509    
## as.factor(VID)78693       0.580813   0.646215   0.899 0.368764    
## as.factor(VID)78694      -0.107306   0.592706  -0.181 0.856332    
## as.factor(VID)78695       0.085388   0.614725   0.139 0.889526    
## as.factor(VID)78696       0.225318   0.629479   0.358 0.720385    
## as.factor(VID)78698       1.345215   0.762844   1.763 0.077829 .  
## as.factor(VID)78699       0.021065   0.615911   0.034 0.972717    
## as.factor(VID)78700       0.382819   0.676908   0.566 0.571706    
## as.factor(VID)78701      -0.482984   0.583569  -0.828 0.407875    
## as.factor(VID)78703       0.555820   0.645901   0.861 0.389494    
## as.factor(VID)78704       1.114903   0.705257   1.581 0.113913    
## as.factor(VID)78705       0.442459   0.625850   0.707 0.479584    
## as.factor(VID)78706       0.363225   0.648378   0.560 0.575339    
## as.factor(VID)78708       0.059886   0.632239   0.095 0.924536    
## as.factor(VID)78709       0.166935   0.651516   0.256 0.797777    
## as.factor(VID)78711       0.703494   0.670946   1.049 0.294403    
## as.factor(VID)78712       0.108207   0.630624   0.172 0.863762    
## as.factor(VID)78713       1.321680   0.869359   1.520 0.128437    
## as.factor(VID)78717       1.671492   0.865589   1.931 0.053477 .  
## as.factor(VID)78718       0.189608   0.629416   0.301 0.763227    
## as.factor(VID)78739       0.275108   0.628039   0.438 0.661355    
## as.factor(VID)78741       0.452720   0.647100   0.700 0.484169    
## as.factor(VID)78742      -0.344545   0.588934  -0.585 0.558526    
## as.factor(VID)78744      -0.066329   0.604724  -0.110 0.912660    
## as.factor(VID)78745       0.187966   0.614178   0.306 0.759570    
## as.factor(VID)78746       0.215480   0.629842   0.342 0.732263    
## as.factor(VID)78748       0.116072   0.614800   0.189 0.850253    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 37106  on 47598  degrees of freedom
## Residual deviance: 32845  on 46789  degrees of freedom
## AIC: 34465
## 
## Number of Fisher Scoring iterations: 16
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 6.8991, df = 8, p-value = 0.5476
148
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 148))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     C002_01, family = "binomial", data = child_ica_dummy %>% 
##     filter(DID == 148))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1602   0.4780   0.5202   0.5651   0.6721  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              2.08964    0.22845   9.147   <2e-16 ***
## n_children_in_household -0.05985    0.04872  -1.228    0.219    
## PR004_PR009_01           0.20142    0.15237   1.322    0.186    
## C002_01                 -0.29794    0.13730  -2.170    0.030 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1462.2  on 1851  degrees of freedom
## Residual deviance: 1454.1  on 1848  degrees of freedom
## AIC: 1462.1
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 0.70355, df = 8, p-value = 0.9995
151
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 151))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     C002_01, family = "binomial", data = child_ica_dummy %>% 
##     filter(DID == 151))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0776   0.5003   0.5051   0.5338   0.7164  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              1.42858    0.25219   5.665 1.47e-08 ***
## n_children_in_household -0.01015    0.05416  -0.187 0.851379    
## PR004_PR009_01           0.61702    0.16072   3.839 0.000123 ***
## C002_01                 -0.12832    0.13800  -0.930 0.352425    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1445.1  on 1793  degrees of freedom
## Residual deviance: 1430.1  on 1790  degrees of freedom
## AIC: 1438.1
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 26.905, df = 8, p-value = 0.0007342
260
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 260))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     C002_01, family = "binomial", data = child_ica_dummy %>% 
##     filter(DID == 260))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1956   0.4753   0.5348   0.5847   0.7639  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              2.48695    0.21355  11.646   <2e-16 ***
## n_children_in_household -0.08535    0.03699  -2.308   0.0210 *  
## PR004_PR009_01          -0.27317    0.14617  -1.869   0.0616 .  
## C002_01                 -0.27779    0.12815  -2.168   0.0302 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1650.2  on 2004  degrees of freedom
## Residual deviance: 1636.2  on 2001  degrees of freedom
## AIC: 1644.2
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 15.649, df = 8, p-value = 0.0477
260, VID
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + factor(VID), family = "binomial", data = child_ica_dummy %>% filter(DID == 260))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     C002_01 + factor(VID), family = "binomial", data = child_ica_dummy %>% 
##     filter(DID == 260))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.98580   0.00014   0.42879   0.57612   1.18235  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)               1.67422    0.48297   3.466 0.000527 ***
## n_children_in_household   0.03091    0.04526   0.683 0.494630    
## PR004_PR009_01            0.06956    0.18637   0.373 0.708963    
## C002_01                  -0.12462    0.14000  -0.890 0.373392    
## factor(VID)47295         -1.07596    0.53590  -2.008 0.044668 *  
## factor(VID)47296          2.51948    1.10231   2.286 0.022276 *  
## factor(VID)47297         -0.54884    0.54600  -1.005 0.314801    
## factor(VID)47298          0.40838    0.64832   0.630 0.528759    
## factor(VID)47299         -0.03990    0.55358  -0.072 0.942536    
## factor(VID)47300         -0.94556    0.55578  -1.701 0.088881 .  
## factor(VID)47301          0.12755    0.57501   0.222 0.824453    
## factor(VID)47302          2.28506    1.10787   2.063 0.039154 *  
## factor(VID)47303         -1.72351    0.50253  -3.430 0.000604 ***
## factor(VID)47304         16.81541  855.93786   0.020 0.984326    
## factor(VID)47305          0.31982    0.62245   0.514 0.607388    
## factor(VID)47306         -0.13887    0.57337  -0.242 0.808632    
## factor(VID)47308          0.81913    0.64618   1.268 0.204919    
## factor(VID)47312         -0.76830    0.53045  -1.448 0.147507    
## factor(VID)47313          0.98803    0.68135   1.450 0.147027    
## factor(VID)47318         -0.31342    0.53646  -0.584 0.559061    
## factor(VID)47327          2.58615    1.10413   2.342 0.019168 *  
## factor(VID)47332          0.42155    0.59249   0.711 0.476779    
## factor(VID)47338          0.09982    0.57314   0.174 0.861735    
## factor(VID)47339         -1.50129    0.51737  -2.902 0.003711 ** 
## factor(VID)47345         16.82728  856.13930   0.020 0.984319    
## factor(VID)47352          0.19564    0.56564   0.346 0.729437    
## factor(VID)47355         16.76490  802.63292   0.021 0.983335    
## factor(VID)47356          0.52232    0.62125   0.841 0.400484    
## factor(VID)47409         -0.49782    0.53881  -0.924 0.355524    
## factor(VID)47582         -0.63324    0.52455  -1.207 0.227346    
## factor(VID)57636         16.81954  863.54271   0.019 0.984460    
## factor(VID)57643          2.31954    1.10185   2.105 0.035279 *  
## factor(VID)57644          0.59762    0.64630   0.925 0.355130    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1650.2  on 2004  degrees of freedom
## Residual deviance: 1365.7  on 1972  degrees of freedom
## AIC: 1431.7
## 
## Number of Fisher Scoring iterations: 17
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 2.569, df = 8, p-value = 0.9584

Without dists_not_fit. GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)

glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy %>% filter(!DID %in% dists_not_fit))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 + 
##     C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy %>% 
##     filter(!DID %in% dists_not_fit))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5291  -1.1412   0.6592   0.8972   1.7474  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              2.20023    0.08886  24.761  < 2e-16 ***
## n_children_in_household -0.03221    0.00342  -9.417  < 2e-16 ***
## PR004_PR009_01           0.36964    0.01108  33.351  < 2e-16 ***
## C002_01                 -0.73250    0.01017 -72.036  < 2e-16 ***
## as.factor(DID)147       -0.61110    0.10692  -5.715 1.09e-08 ***
## as.factor(DID)149       -0.80774    0.10286  -7.853 4.07e-15 ***
## as.factor(DID)150       -0.78424    0.10201  -7.688 1.50e-14 ***
## as.factor(DID)153       -0.90444    0.10406  -8.692  < 2e-16 ***
## as.factor(DID)154       -0.68180    0.10568  -6.452 1.11e-10 ***
## as.factor(DID)155       -0.84481    0.10119  -8.349  < 2e-16 ***
## as.factor(DID)156       -1.33487    0.11582 -11.525  < 2e-16 ***
## as.factor(DID)157       -0.45598    0.11098  -4.108 3.98e-05 ***
## as.factor(DID)159       -1.32982    0.10655 -12.481  < 2e-16 ***
## as.factor(DID)160       -0.73750    0.10489  -7.031 2.05e-12 ***
## as.factor(DID)161       -1.27916    0.09762 -13.103  < 2e-16 ***
## as.factor(DID)164       -0.57774    0.11384  -5.075 3.87e-07 ***
## as.factor(DID)165       -0.98013    0.10429  -9.398  < 2e-16 ***
## as.factor(DID)166       -0.83499    0.10473  -7.973 1.55e-15 ***
## as.factor(DID)167        0.50880    0.13040   3.902 9.55e-05 ***
## as.factor(DID)169       -1.72476    0.09928 -17.373  < 2e-16 ***
## as.factor(DID)170       -0.47695    0.10872  -4.387 1.15e-05 ***
## as.factor(DID)171       -0.44109    0.11072  -3.984 6.78e-05 ***
## as.factor(DID)172       -0.63932    0.10654  -6.001 1.96e-09 ***
## as.factor(DID)174       -0.96366    0.10561  -9.125  < 2e-16 ***
## as.factor(DID)175       -1.33897    0.10151 -13.191  < 2e-16 ***
## as.factor(DID)176       -0.48230    0.11479  -4.201 2.65e-05 ***
## as.factor(DID)177       -0.85401    0.10314  -8.280  < 2e-16 ***
## as.factor(DID)179       -0.72202    0.10558  -6.839 7.98e-12 ***
## as.factor(DID)180       -0.68083    0.10615  -6.414 1.42e-10 ***
## as.factor(DID)182       -1.39805    0.10043 -13.920  < 2e-16 ***
## as.factor(DID)183       -0.65956    0.10778  -6.120 9.39e-10 ***
## as.factor(DID)184       -1.30777    0.10005 -13.071  < 2e-16 ***
## as.factor(DID)186       -1.69771    0.09953 -17.058  < 2e-16 ***
## as.factor(DID)187       -1.46210    0.10117 -14.452  < 2e-16 ***
## as.factor(DID)188       -1.30548    0.10037 -13.006  < 2e-16 ***
## as.factor(DID)189       -0.35890    0.10834  -3.313 0.000924 ***
## as.factor(DID)190       -0.99623    0.10865  -9.169  < 2e-16 ***
## as.factor(DID)191       -0.90900    0.10845  -8.382  < 2e-16 ***
## as.factor(DID)192       -1.06589    0.10269 -10.380  < 2e-16 ***
## as.factor(DID)194       -1.68630    0.09992 -16.876  < 2e-16 ***
## as.factor(DID)195       -1.88739    0.10107 -18.674  < 2e-16 ***
## as.factor(DID)196       -0.70672    0.10997  -6.426 1.31e-10 ***
## as.factor(DID)197       -1.43308    0.10084 -14.211  < 2e-16 ***
## as.factor(DID)198       -1.79500    0.09975 -17.996  < 2e-16 ***
## as.factor(DID)200       -1.46069    0.10294 -14.190  < 2e-16 ***
## as.factor(DID)202       -1.48817    0.10096 -14.740  < 2e-16 ***
## as.factor(DID)203       -1.44779    0.09921 -14.593  < 2e-16 ***
## as.factor(DID)204       -0.89171    0.10343  -8.621  < 2e-16 ***
## as.factor(DID)205       -1.59378    0.09841 -16.196  < 2e-16 ***
## as.factor(DID)206       -1.65528    0.09837 -16.827  < 2e-16 ***
## as.factor(DID)207       -2.02987    0.09995 -20.309  < 2e-16 ***
## as.factor(DID)208       -1.51565    0.09950 -15.233  < 2e-16 ***
## as.factor(DID)209       -1.03418    0.10817  -9.560  < 2e-16 ***
## as.factor(DID)210       -1.72023    0.09790 -17.572  < 2e-16 ***
## as.factor(DID)211       -1.03761    0.10795  -9.611  < 2e-16 ***
## as.factor(DID)212       -1.34488    0.10228 -13.149  < 2e-16 ***
## as.factor(DID)213       -1.58217    0.09868 -16.033  < 2e-16 ***
## as.factor(DID)214       -2.10360    0.09920 -21.205  < 2e-16 ***
## as.factor(DID)215       -1.35788    0.09697 -14.003  < 2e-16 ***
## as.factor(DID)216       -1.68497    0.10160 -16.585  < 2e-16 ***
## as.factor(DID)217       -1.71022    0.09997 -17.107  < 2e-16 ***
## as.factor(DID)218       -1.64404    0.09735 -16.889  < 2e-16 ***
## as.factor(DID)219       -1.54732    0.10069 -15.366  < 2e-16 ***
## as.factor(DID)220       -2.09984    0.09644 -21.774  < 2e-16 ***
## as.factor(DID)221       -2.45952    0.10142 -24.251  < 2e-16 ***
## as.factor(DID)222       -1.69695    0.09785 -17.341  < 2e-16 ***
## as.factor(DID)223       -1.61343    0.10554 -15.288  < 2e-16 ***
## as.factor(DID)224       -2.15683    0.09912 -21.759  < 2e-16 ***
## as.factor(DID)225       -1.86448    0.09974 -18.693  < 2e-16 ***
## as.factor(DID)226       -1.38124    0.10295 -13.417  < 2e-16 ***
## as.factor(DID)227       -1.67171    0.09806 -17.047  < 2e-16 ***
## as.factor(DID)228       -2.25942    0.10320 -21.893  < 2e-16 ***
## as.factor(DID)229       -1.96803    0.10194 -19.306  < 2e-16 ***
## as.factor(DID)230       -2.14701    0.09950 -21.579  < 2e-16 ***
## as.factor(DID)231       -1.40315    0.09889 -14.188  < 2e-16 ***
## as.factor(DID)233       -2.27841    0.10437 -21.830  < 2e-16 ***
## as.factor(DID)234       -1.58568    0.10526 -15.065  < 2e-16 ***
## as.factor(DID)235       -0.83483    0.10293  -8.111 5.02e-16 ***
## as.factor(DID)236       -1.10526    0.10314 -10.717  < 2e-16 ***
## as.factor(DID)237       -0.76287    0.10610  -7.190 6.46e-13 ***
## as.factor(DID)239       -0.89327    0.10257  -8.708  < 2e-16 ***
## as.factor(DID)240       -1.24717    0.10232 -12.189  < 2e-16 ***
## as.factor(DID)241       -2.37464    0.09837 -24.140  < 2e-16 ***
## as.factor(DID)242       -1.09923    0.10351 -10.619  < 2e-16 ***
## as.factor(DID)243       -1.07306    0.10870  -9.872  < 2e-16 ***
## as.factor(DID)244       -0.56597    0.11096  -5.101 3.38e-07 ***
## as.factor(DID)246       -1.71773    0.10021 -17.142  < 2e-16 ***
## as.factor(DID)247       -1.23790    0.10783 -11.480  < 2e-16 ***
## as.factor(DID)248       -0.94198    0.09993  -9.427  < 2e-16 ***
## as.factor(DID)250       -0.72232    0.10526  -6.862 6.77e-12 ***
## as.factor(DID)251       -0.68896    0.10687  -6.447 1.14e-10 ***
## as.factor(DID)252       -0.91843    0.10390  -8.840  < 2e-16 ***
## as.factor(DID)253       -0.51896    0.11209  -4.630 3.66e-06 ***
## as.factor(DID)254       -1.76683    0.11543 -15.306  < 2e-16 ***
## as.factor(DID)255       -0.86631    0.10906  -7.943 1.97e-15 ***
## as.factor(DID)257        0.49870    0.14055   3.548 0.000388 ***
## as.factor(DID)258       -1.57776    0.09915 -15.912  < 2e-16 ***
## as.factor(DID)259       -0.84587    0.10732  -7.882 3.23e-15 ***
## as.factor(DID)261       -2.30250    0.09833 -23.416  < 2e-16 ***
## as.factor(DID)262       -0.72491    0.10371  -6.990 2.75e-12 ***
## as.factor(DID)263       -0.41112    0.10755  -3.822 0.000132 ***
## as.factor(DID)264       -0.44120    0.10580  -4.170 3.04e-05 ***
## as.factor(DID)266        0.61879    0.13022   4.752 2.02e-06 ***
## as.factor(DID)268       -1.12253    0.10429 -10.763  < 2e-16 ***
## as.factor(DID)275       -1.11609    0.10208 -10.933  < 2e-16 ***
## as.factor(DID)278       -0.79219    0.09862  -8.033 9.50e-16 ***
## as.factor(DID)279       -1.83698    0.10016 -18.340  < 2e-16 ***
## as.factor(DID)280       -1.05514    0.10240 -10.304  < 2e-16 ***
## as.factor(DID)281       -0.82032    0.10204  -8.039 9.05e-16 ***
## as.factor(DID)282       -0.78629    0.10925  -7.197 6.16e-13 ***
## as.factor(DID)284       -0.78193    0.10472  -7.467 8.19e-14 ***
## as.factor(DID)287       -1.04173    0.10478  -9.942  < 2e-16 ***
## as.factor(DID)289       -0.88492    0.10127  -8.738  < 2e-16 ***
## as.factor(DID)290       -1.05728    0.10444 -10.123  < 2e-16 ***
## as.factor(DID)315       -0.74213    0.11562  -6.419 1.37e-10 ***
## as.factor(DID)316       -1.09485    0.10357 -10.571  < 2e-16 ***
## as.factor(DID)318       -1.66751    0.10549 -15.807  < 2e-16 ***
## as.factor(DID)319       -2.28183    0.09897 -23.056  < 2e-16 ***
## as.factor(DID)320       -1.53198    0.10486 -14.609  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 253748  on 198147  degrees of freedom
## Residual deviance: 232377  on 198029  degrees of freedom
## AIC: 232615
## 
## Number of Fisher Scoring iterations: 5
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 212.13, df = 8, p-value < 2.2e-16
Location dists_not_fit
ica %>% 
  mutate(centroid = st_centroid(geometry),
    x = st_coordinates(centroid)[,1],
    y = st_coordinates(centroid)[,2]) %>% 
    ggplot() +
  geom_sf() +
  geom_point(data = child_ica_dummy,
             aes(x, y, color = DID %in% dists_not_fit)) +
  coord_sf(xlim = c(min(child_ica_dummy$x), max(child_ica_dummy$x)), 
           ylim = c(min(child_ica_dummy$y), max(child_ica_dummy$y))) +
  geom_text(data = child_ica_dummy %>% filter(DID == dists_not_fit),
            aes(x, y, label = DID), check_overlap = TRUE, vjust = -0.5) +
  ggtitle("Distrists, the Data of Which the Model Doesn't Fit Well")
## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.
## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data
## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません
## Warning: Removed 8238 rows containing missing values (geom_point).

fit the momdel for every districts using apply function

All Districts. C003_01 ~ n_children_in_household PR004_PR009_01 + C002_01

fit_all <- sapply(DID_unique, function(id){
  
  glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, 
                   family = "binomial", data = child_ica_dummy %>% filter(DID == id))
  
  # ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
  
  hoslem <- hoslem.test(x = glm_child$y, y = fitted(glm_child))
  data.frame(id, hoslem["p.value"])
}) %>% t()

fit_all <- fit_all %>% as.data.frame()

fit_all$id <- fit_all$id %>% as.numeric()
fit_all$p.value <- fit_all$p.value %>% as.numeric()

fit_all
Districts whose model is not fitted well
fit_all %>% 
  filter(p.value < 0.05)

GLM on Never Enrolled

GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01

glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     PR004_PR009_01, family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.0852  -0.7787  -0.6059  -0.5696   1.9488  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -1.118868   0.014509 -77.115   <2e-16 ***
## n_children_in_household  0.026920   0.003095   8.699   <2e-16 ***
## C002_01                  0.575131   0.009503  60.518   <2e-16 ***
## PR004_PR009_01          -0.644803   0.009532 -67.645   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 276273  on 245746  degrees of freedom
## Residual deviance: 267986  on 245743  degrees of freedom
## AIC: 267994
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 402.3, df = 8, p-value < 2.2e-16

GLM C003_1_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01

glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy)

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + H002_1_01 + 
##     H002_2_01, family = "binomial", data = child_ica_dummy)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9610  -1.2179   0.6798   0.8733   1.2973  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              1.817833   0.015566  116.78   <2e-16 ***
## n_children_in_household -0.053166   0.002945  -18.05   <2e-16 ***
## C002_01                 -0.579749   0.009071  -63.91   <2e-16 ***
## H002_1_01               -0.930780   0.012499  -74.47   <2e-16 ***
## H002_2_01               -0.257998   0.013052  -19.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 287756  on 245742  degrees of freedom
## AIC: 287766
## 
## Number of Fisher Scoring iterations: 4
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 324.32, df = 8, p-value < 2.2e-16

Hunza. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01

glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% 
##     filter(DID == 266))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.3664  -0.3453  -0.3340  -0.2888   2.6065  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -3.39338    0.45360  -7.481 7.38e-14 ***
## n_children_in_household  0.03063    0.08648   0.354    0.723    
## C002_01                  0.07590    0.22551   0.337    0.736    
## PR004_PR009_01           0.43561    0.30655   1.421    0.155    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 659.67  on 1609  degrees of freedom
## Residual deviance: 657.35  on 1606  degrees of freedom
## AIC: 665.35
## 
## Number of Fisher Scoring iterations: 6
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 4.5829, df = 8, p-value = 0.8011

Hunza. GLM C003_1_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01

glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy %>% 
##     filter(DID == 266))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4753  -0.3872  -0.2587  -0.2500   2.6596  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -2.66133    0.32680  -8.144 3.83e-16 ***
## n_children_in_household  0.01893    0.08399   0.225 0.821716    
## C002_01                  0.07028    0.22687   0.310 0.756715    
## H002_1_01                0.35399    0.32063   1.104 0.269586    
## H002_2_01               -0.86483    0.25322  -3.415 0.000637 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 659.67  on 1609  degrees of freedom
## Residual deviance: 641.33  on 1605  degrees of freedom
## AIC: 651.33
## 
## Number of Fisher Scoring iterations: 6
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 6.7098, df = 8, p-value = 0.5682

GLM Age >= 5. GLM C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01

glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, 
##     family = "binomial", data = child_ica_dummy %>% filter(C001 >= 
##         5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2955   0.4095   0.5484   0.7001   1.4258  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              1.651863   0.017175   96.18   <2e-16 ***
## n_children_in_household -0.123601   0.003608  -34.26   <2e-16 ***
## C002_01                 -0.735895   0.011276  -65.26   <2e-16 ***
## PR004_PR009_01           1.032038   0.011369   90.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 212206  on 208249  degrees of freedom
## Residual deviance: 198026  on 208246  degrees of freedom
## AIC: 198034
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 96.347, df = 8, p-value < 2.2e-16

GLM Age >= 5. GLM C003_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID)

glm_child <- glm(C003_01 ~ 
                   n_children_in_household + 
                   C002_01 + 
                   PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID), 
                 family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_only_01 + 
##     PR009_only_01 + PR004_PR009_both_01 + as.factor(DID), family = "binomial", 
##     data = child_ica_dummy %>% filter(C001 >= 5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.3709   0.1728   0.4345   0.6912   1.8018  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              2.899487   0.119889  24.185  < 2e-16 ***
## n_children_in_household -0.096449   0.004058 -23.766  < 2e-16 ***
## C002_01                 -0.880439   0.012053 -73.048  < 2e-16 ***
## PR004_only_01            0.372396   0.033743  11.036  < 2e-16 ***
## PR009_only_01            0.460871   0.015063  30.597  < 2e-16 ***
## PR004_PR009_both_01      0.906314   0.018700  48.465  < 2e-16 ***
## as.factor(DID)147       -0.694718   0.141050  -4.925 8.42e-07 ***
## as.factor(DID)148       -0.102451   0.148896  -0.688 0.491408    
## as.factor(DID)149       -0.901388   0.135608  -6.647 2.99e-11 ***
## as.factor(DID)150       -0.793476   0.135270  -5.866 4.47e-09 ***
## as.factor(DID)151       -0.022429   0.156431  -0.143 0.885992    
## as.factor(DID)152        0.039072   0.169467   0.231 0.817657    
## as.factor(DID)153       -0.963592   0.137924  -6.986 2.82e-12 ***
## as.factor(DID)154       -0.757312   0.140837  -5.377 7.56e-08 ***
## as.factor(DID)155       -0.873652   0.134073  -6.516 7.21e-11 ***
## as.factor(DID)156       -1.535559   0.151893 -10.109  < 2e-16 ***
## as.factor(DID)157       -0.320648   0.151937  -2.110 0.034824 *  
## as.factor(DID)158       -0.275462   0.146546  -1.880 0.060149 .  
## as.factor(DID)159       -1.532667   0.140352 -10.920  < 2e-16 ***
## as.factor(DID)160       -0.862810   0.137645  -6.268 3.65e-10 ***
## as.factor(DID)161       -1.463378   0.128794 -11.362  < 2e-16 ***
## as.factor(DID)162        0.117193   0.177534   0.660 0.509180    
## as.factor(DID)163       -0.168766   0.169136  -0.998 0.318369    
## as.factor(DID)164       -0.913588   0.149970  -6.092 1.12e-09 ***
## as.factor(DID)165       -1.069020   0.137551  -7.772 7.74e-15 ***
## as.factor(DID)166       -0.904914   0.138992  -6.511 7.49e-11 ***
## as.factor(DID)167        0.445641   0.175981   2.532 0.011331 *  
## as.factor(DID)169       -1.913391   0.129797 -14.741  < 2e-16 ***
## as.factor(DID)170       -0.431530   0.147381  -2.928 0.003412 ** 
## as.factor(DID)171       -0.387056   0.150422  -2.573 0.010078 *  
## as.factor(DID)172       -0.553998   0.144240  -3.841 0.000123 ***
## as.factor(DID)173       -0.280344   0.146574  -1.913 0.055794 .  
## as.factor(DID)174       -0.974149   0.140699  -6.924 4.40e-12 ***
## as.factor(DID)175       -1.542380   0.133271 -11.573  < 2e-16 ***
## as.factor(DID)176        0.411946   0.205760   2.002 0.045277 *  
## as.factor(DID)177       -0.986479   0.135135  -7.300 2.88e-13 ***
## as.factor(DID)178        0.729015   0.185628   3.927 8.59e-05 ***
## as.factor(DID)179       -0.337996   0.149653  -2.259 0.023913 *  
## as.factor(DID)180       -0.640184   0.141685  -4.518 6.23e-06 ***
## as.factor(DID)181       -0.339986   0.152590  -2.228 0.025874 *  
## as.factor(DID)182       -1.587793   0.131594 -12.066  < 2e-16 ***
## as.factor(DID)183       -0.752766   0.140822  -5.346 9.02e-08 ***
## as.factor(DID)184       -1.380532   0.131473 -10.500  < 2e-16 ***
## as.factor(DID)185        0.580322   0.160995   3.605 0.000313 ***
## as.factor(DID)186       -1.604095   0.131283 -12.219  < 2e-16 ***
## as.factor(DID)187       -1.384015   0.133458 -10.370  < 2e-16 ***
## as.factor(DID)188       -1.154858   0.134257  -8.602  < 2e-16 ***
## as.factor(DID)189       -0.297516   0.144937  -2.053 0.040100 *  
## as.factor(DID)190       -1.008322   0.143109  -7.046 1.84e-12 ***
## as.factor(DID)191       -0.880028   0.143283  -6.142 8.15e-10 ***
## as.factor(DID)192       -0.581270   0.145776  -3.987 6.68e-05 ***
## as.factor(DID)193        0.194334   0.149756   1.298 0.194399    
## as.factor(DID)194       -1.833927   0.130417 -14.062  < 2e-16 ***
## as.factor(DID)195       -2.043092   0.131591 -15.526  < 2e-16 ***
## as.factor(DID)196       -0.190307   0.164257  -1.159 0.246623    
## as.factor(DID)197       -1.567823   0.131772 -11.898  < 2e-16 ***
## as.factor(DID)198       -1.950151   0.130308 -14.966  < 2e-16 ***
## as.factor(DID)199        1.316189   0.190052   6.925 4.35e-12 ***
## as.factor(DID)200       -1.577631   0.134445 -11.734  < 2e-16 ***
## as.factor(DID)202       -1.577230   0.133171 -11.844  < 2e-16 ***
## as.factor(DID)203       -1.696145   0.129359 -13.112  < 2e-16 ***
## as.factor(DID)204       -0.789079   0.137557  -5.736 9.67e-09 ***
## as.factor(DID)205       -1.779512   0.129027 -13.792  < 2e-16 ***
## as.factor(DID)206       -1.652202   0.129155 -12.792  < 2e-16 ***
## as.factor(DID)207       -2.149577   0.129544 -16.593  < 2e-16 ***
## as.factor(DID)208       -1.519791   0.130857 -11.614  < 2e-16 ***
## as.factor(DID)209       -0.879885   0.144756  -6.078 1.21e-09 ***
## as.factor(DID)210       -1.784615   0.128359 -13.903  < 2e-16 ***
## as.factor(DID)211       -1.012345   0.142460  -7.106 1.19e-12 ***
## as.factor(DID)212       -1.593997   0.131690 -12.104  < 2e-16 ***
## as.factor(DID)213       -1.653284   0.129759 -12.741  < 2e-16 ***
## as.factor(DID)214       -2.054347   0.130051 -15.796  < 2e-16 ***
## as.factor(DID)215       -1.057076   0.129511  -8.162 3.29e-16 ***
## as.factor(DID)216       -1.899444   0.131809 -14.411  < 2e-16 ***
## as.factor(DID)217       -1.722100   0.131150 -13.131  < 2e-16 ***
## as.factor(DID)218       -1.713569   0.128130 -13.374  < 2e-16 ***
## as.factor(DID)219       -1.758501   0.131643 -13.358  < 2e-16 ***
## as.factor(DID)220       -2.086986   0.126605 -16.484  < 2e-16 ***
## as.factor(DID)221       -2.554577   0.130914 -19.513  < 2e-16 ***
## as.factor(DID)222       -1.868063   0.128090 -14.584  < 2e-16 ***
## as.factor(DID)223       -1.533269   0.139598 -10.983  < 2e-16 ***
## as.factor(DID)224       -2.167135   0.129893 -16.684  < 2e-16 ***
## as.factor(DID)225       -2.111673   0.130464 -16.186  < 2e-16 ***
## as.factor(DID)226       -1.477990   0.133501 -11.071  < 2e-16 ***
## as.factor(DID)227       -1.591459   0.128905 -12.346  < 2e-16 ***
## as.factor(DID)228       -2.540654   0.131996 -19.248  < 2e-16 ***
## as.factor(DID)229       -1.854078   0.132873 -13.954  < 2e-16 ***
## as.factor(DID)230       -2.185344   0.130044 -16.805  < 2e-16 ***
## as.factor(DID)231       -1.679584   0.129106 -13.009  < 2e-16 ***
## as.factor(DID)232       -0.407614   0.141104  -2.889 0.003868 ** 
## as.factor(DID)233       -2.526160   0.133969 -18.856  < 2e-16 ***
## as.factor(DID)234       -1.544160   0.137621 -11.220  < 2e-16 ***
## as.factor(DID)235       -0.878094   0.136064  -6.454 1.09e-10 ***
## as.factor(DID)236       -1.028567   0.137486  -7.481 7.36e-14 ***
## as.factor(DID)237       -0.623776   0.142433  -4.379 1.19e-05 ***
## as.factor(DID)238        2.161672   0.278170   7.771 7.78e-15 ***
## as.factor(DID)239       -0.230980   0.147443  -1.567 0.117214    
## as.factor(DID)240       -0.943068   0.138349  -6.817 9.32e-12 ***
## as.factor(DID)241       -2.018914   0.130733 -15.443  < 2e-16 ***
## as.factor(DID)242       -0.974203   0.137943  -7.062 1.64e-12 ***
## as.factor(DID)243        1.145910   0.286737   3.996 6.43e-05 ***
## as.factor(DID)244       -0.481463   0.151248  -3.183 0.001456 ** 
## as.factor(DID)245        0.500859   0.176576   2.837 0.004561 ** 
## as.factor(DID)246       -1.621224   0.133290 -12.163  < 2e-16 ***
## as.factor(DID)247       -1.320995   0.140180  -9.424  < 2e-16 ***
## as.factor(DID)248       -0.876565   0.132441  -6.619 3.63e-11 ***
## as.factor(DID)249        0.175049   0.169357   1.034 0.301320    
## as.factor(DID)250       -0.673176   0.138847  -4.848 1.25e-06 ***
## as.factor(DID)251       -0.507268   0.144643  -3.507 0.000453 ***
## as.factor(DID)252       -0.844213   0.138085  -6.114 9.73e-10 ***
## as.factor(DID)253       -0.599220   0.147988  -4.049 5.14e-05 ***
## as.factor(DID)254       -1.750979   0.150685 -11.620  < 2e-16 ***
## as.factor(DID)255       -0.901385   0.144889  -6.221 4.93e-10 ***
## as.factor(DID)256        0.221349   0.152148   1.455 0.145719    
## as.factor(DID)257        0.304660   0.191405   1.592 0.111452    
## as.factor(DID)258       -1.778704   0.130221 -13.659  < 2e-16 ***
## as.factor(DID)259        0.797673   0.195064   4.089 4.33e-05 ***
## as.factor(DID)260        0.270849   0.153340   1.766 0.077342 .  
## as.factor(DID)261       -2.266533   0.128398 -17.652  < 2e-16 ***
## as.factor(DID)262       -0.302815   0.143168  -2.115 0.034420 *  
## as.factor(DID)263        0.213236   0.156933   1.359 0.174221    
## as.factor(DID)264        0.038498   0.148922   0.259 0.796016    
## as.factor(DID)265        0.690491   0.178925   3.859 0.000114 ***
## as.factor(DID)266        0.931776   0.195706   4.761 1.93e-06 ***
## as.factor(DID)267        0.650586   0.174413   3.730 0.000191 ***
## as.factor(DID)268       -0.786670   0.148931  -5.282 1.28e-07 ***
## as.factor(DID)269        1.776172   0.285555   6.220 4.97e-10 ***
## as.factor(DID)270        0.689285   0.172000   4.007 6.14e-05 ***
## as.factor(DID)271        1.341308   0.237725   5.642 1.68e-08 ***
## as.factor(DID)272        1.753460   0.278089   6.305 2.87e-10 ***
## as.factor(DID)273        0.580839   0.185768   3.127 0.001768 ** 
## as.factor(DID)274        1.021699   0.250745   4.075 4.61e-05 ***
## as.factor(DID)275       -1.036634   0.138275  -7.497 6.53e-14 ***
## as.factor(DID)276        2.105320   0.277851   7.577 3.53e-14 ***
## as.factor(DID)277       -0.269683   0.203955  -1.322 0.186078    
## as.factor(DID)278       -0.375287   0.133932  -2.802 0.005077 ** 
## as.factor(DID)279       -1.770222   0.131573 -13.454  < 2e-16 ***
## as.factor(DID)280       -0.668344   0.141198  -4.733 2.21e-06 ***
## as.factor(DID)281       -0.009726   0.149903  -0.065 0.948266    
## as.factor(DID)282       -0.496916   0.151684  -3.276 0.001053 ** 
## as.factor(DID)284       -0.713572   0.138794  -5.141 2.73e-07 ***
## as.factor(DID)287       -0.698540   0.144448  -4.836 1.33e-06 ***
## as.factor(DID)289       -1.070184   0.132693  -8.065 7.32e-16 ***
## as.factor(DID)290       -0.995415   0.138011  -7.213 5.49e-13 ***
## as.factor(DID)315       -0.581439   0.162863  -3.570 0.000357 ***
## as.factor(DID)316       -1.327520   0.134923  -9.839  < 2e-16 ***
## as.factor(DID)318       -1.795890   0.135705 -13.234  < 2e-16 ***
## as.factor(DID)319       -2.452244   0.128858 -19.031  < 2e-16 ***
## as.factor(DID)320       -1.793792   0.135743 -13.215  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 212206  on 208249  degrees of freedom
## Residual deviance: 177115  on 208101  degrees of freedom
## AIC: 177413
## 
## Number of Fisher Scoring iterations: 7
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 102.52, df = 8, p-value < 2.2e-16
AIC
extractAIC(glm_child)
## [1]    149.0 177412.6
BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))
## [1]    149.0 178939.3
effectiveness of explanatory variables
glm_child_null <- glm(C003_01 ~ 1, family = "binomial", 
                      data = child_ica_dummy %>% filter(C001 >= 5))
anova(glm_child_null, glm_child, test = "Chisq")
multicolinearity
vif(glm_child)
##                             GVIF  Df GVIF^(1/(2*Df))
## n_children_in_household 1.148529   1        1.071694
## C002_01                 1.032871   1        1.016302
## PR004_only_01           1.049785   1        1.024590
## PR009_only_01           1.211205   1        1.100548
## PR004_PR009_both_01     1.310188   1        1.144634
## as.factor(DID)          1.542294 143        1.001516

GLM Age >= 5. GLM C003_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(C001) + as.factor(DID)

glm_child <- glm(C003_01 ~ 
                   n_children_in_household + 
                   C002_01 + 
                   PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + 
                   as.factor(C001) + as.factor(DID), 
                 family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_only_01 + 
##     PR009_only_01 + PR004_PR009_both_01 + as.factor(C001) + as.factor(DID), 
##     family = "binomial", data = child_ica_dummy %>% filter(C001 >= 
##         5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.4751   0.1470   0.3916   0.6568   2.2667  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              2.236346   0.122653  18.233  < 2e-16 ***
## n_children_in_household -0.106627   0.004158 -25.644  < 2e-16 ***
## C002_01                 -0.930098   0.012449 -74.712  < 2e-16 ***
## PR004_only_01            0.387283   0.034732  11.150  < 2e-16 ***
## PR009_only_01            0.478216   0.015487  30.878  < 2e-16 ***
## PR004_PR009_both_01      0.942980   0.019174  49.180  < 2e-16 ***
## as.factor(C001)6         0.838229   0.024771  33.840  < 2e-16 ***
## as.factor(C001)7         1.324418   0.026334  50.294  < 2e-16 ***
## as.factor(C001)8         1.428928   0.026253  54.429  < 2e-16 ***
## as.factor(C001)9         1.495093   0.028918  51.702  < 2e-16 ***
## as.factor(C001)10        1.301743   0.025936  50.191  < 2e-16 ***
## as.factor(C001)11        1.201043   0.031233  38.454  < 2e-16 ***
## as.factor(C001)12        0.920281   0.027075  33.990  < 2e-16 ***
## as.factor(C001)13        0.689955   0.028503  24.207  < 2e-16 ***
## as.factor(C001)14        0.381012   0.027060  14.080  < 2e-16 ***
## as.factor(C001)15        0.161030   0.029101   5.534 3.14e-08 ***
## as.factor(C001)16       -0.089958   0.027385  -3.285 0.001020 ** 
## as.factor(DID)147       -0.784418   0.143471  -5.467 4.57e-08 ***
## as.factor(DID)148       -0.093647   0.151372  -0.619 0.536144    
## as.factor(DID)149       -0.981176   0.138000  -7.110 1.16e-12 ***
## as.factor(DID)150       -0.837673   0.137748  -6.081 1.19e-09 ***
## as.factor(DID)151       -0.092873   0.158868  -0.585 0.558824    
## as.factor(DID)152        0.031874   0.171944   0.185 0.852937    
## as.factor(DID)153       -1.028301   0.140457  -7.321 2.46e-13 ***
## as.factor(DID)154       -0.807692   0.143409  -5.632 1.78e-08 ***
## as.factor(DID)155       -0.951648   0.136528  -6.970 3.16e-12 ***
## as.factor(DID)156       -1.666966   0.155141 -10.745  < 2e-16 ***
## as.factor(DID)157       -0.348356   0.154313  -2.257 0.023979 *  
## as.factor(DID)158       -0.309890   0.149030  -2.079 0.037582 *  
## as.factor(DID)159       -1.664872   0.142856 -11.654  < 2e-16 ***
## as.factor(DID)160       -0.914787   0.140183  -6.526 6.77e-11 ***
## as.factor(DID)161       -1.539884   0.131147 -11.742  < 2e-16 ***
## as.factor(DID)162        0.078467   0.179825   0.436 0.662582    
## as.factor(DID)163       -0.219480   0.171823  -1.277 0.201475    
## as.factor(DID)164       -0.988283   0.152603  -6.476 9.41e-11 ***
## as.factor(DID)165       -1.142611   0.140204  -8.150 3.65e-16 ***
## as.factor(DID)166       -1.064827   0.141448  -7.528 5.15e-14 ***
## as.factor(DID)167        0.451268   0.178252   2.532 0.011354 *  
## as.factor(DID)169       -2.042023   0.132290 -15.436  < 2e-16 ***
## as.factor(DID)170       -0.461501   0.150011  -3.076 0.002095 ** 
## as.factor(DID)171       -0.383486   0.152999  -2.506 0.012195 *  
## as.factor(DID)172       -0.615547   0.146642  -4.198 2.70e-05 ***
## as.factor(DID)173       -0.310071   0.148937  -2.082 0.037352 *  
## as.factor(DID)174       -1.056767   0.143288  -7.375 1.64e-13 ***
## as.factor(DID)175       -1.672568   0.135814 -12.315  < 2e-16 ***
## as.factor(DID)176        0.391751   0.207912   1.884 0.059536 .  
## as.factor(DID)177       -1.056060   0.137646  -7.672 1.69e-14 ***
## as.factor(DID)178        0.748058   0.187649   3.986 6.71e-05 ***
## as.factor(DID)179       -0.365307   0.152115  -2.402 0.016327 *  
## as.factor(DID)180       -0.676729   0.144323  -4.689 2.75e-06 ***
## as.factor(DID)181       -0.284123   0.154858  -1.835 0.066546 .  
## as.factor(DID)182       -1.743768   0.134112 -13.002  < 2e-16 ***
## as.factor(DID)183       -0.725167   0.143396  -5.057 4.26e-07 ***
## as.factor(DID)184       -1.461445   0.134038 -10.903  < 2e-16 ***
## as.factor(DID)185        0.410339   0.163317   2.513 0.011987 *  
## as.factor(DID)186       -1.699844   0.133863 -12.698  < 2e-16 ***
## as.factor(DID)187       -1.530893   0.136151 -11.244  < 2e-16 ***
## as.factor(DID)188       -1.247158   0.136706  -9.123  < 2e-16 ***
## as.factor(DID)189       -0.435398   0.147278  -2.956 0.003114 ** 
## as.factor(DID)190       -1.037103   0.145935  -7.107 1.19e-12 ***
## as.factor(DID)191       -0.928599   0.146031  -6.359 2.03e-10 ***
## as.factor(DID)192       -0.605415   0.148511  -4.077 4.57e-05 ***
## as.factor(DID)193        0.013415   0.152164   0.088 0.929746    
## as.factor(DID)194       -1.891979   0.132877 -14.239  < 2e-16 ***
## as.factor(DID)195       -2.160590   0.134138 -16.107  < 2e-16 ***
## as.factor(DID)196       -0.252910   0.166684  -1.517 0.129191    
## as.factor(DID)197       -1.687335   0.134251 -12.569  < 2e-16 ***
## as.factor(DID)198       -2.122290   0.132840 -15.976  < 2e-16 ***
## as.factor(DID)199        1.282946   0.192131   6.677 2.43e-11 ***
## as.factor(DID)200       -1.710652   0.137128 -12.475  < 2e-16 ***
## as.factor(DID)202       -1.719709   0.135765 -12.667  < 2e-16 ***
## as.factor(DID)203       -1.849364   0.131847 -14.027  < 2e-16 ***
## as.factor(DID)204       -0.841368   0.140185  -6.002 1.95e-09 ***
## as.factor(DID)205       -1.893421   0.131421 -14.407  < 2e-16 ***
## as.factor(DID)206       -1.847546   0.131596 -14.040  < 2e-16 ***
## as.factor(DID)207       -2.314375   0.131985 -17.535  < 2e-16 ***
## as.factor(DID)208       -1.621990   0.133352 -12.163  < 2e-16 ***
## as.factor(DID)209       -0.946120   0.147759  -6.403 1.52e-10 ***
## as.factor(DID)210       -1.916745   0.130726 -14.662  < 2e-16 ***
## as.factor(DID)211       -1.116472   0.145225  -7.688 1.50e-14 ***
## as.factor(DID)212       -1.714244   0.134122 -12.781  < 2e-16 ***
## as.factor(DID)213       -1.775314   0.132174 -13.432  < 2e-16 ***
## as.factor(DID)214       -2.233748   0.132613 -16.844  < 2e-16 ***
## as.factor(DID)215       -1.181603   0.132030  -8.949  < 2e-16 ***
## as.factor(DID)216       -2.069586   0.134391 -15.400  < 2e-16 ***
## as.factor(DID)217       -1.806066   0.133744 -13.504  < 2e-16 ***
## as.factor(DID)218       -1.880252   0.130511 -14.407  < 2e-16 ***
## as.factor(DID)219       -1.900769   0.134133 -14.171  < 2e-16 ***
## as.factor(DID)220       -2.227067   0.128999 -17.264  < 2e-16 ***
## as.factor(DID)221       -2.745910   0.133357 -20.591  < 2e-16 ***
## as.factor(DID)222       -1.993950   0.130488 -15.281  < 2e-16 ***
## as.factor(DID)223       -1.603530   0.142620 -11.243  < 2e-16 ***
## as.factor(DID)224       -2.287773   0.132287 -17.294  < 2e-16 ***
## as.factor(DID)225       -2.276112   0.132907 -17.126  < 2e-16 ***
## as.factor(DID)226       -1.564626   0.136238 -11.485  < 2e-16 ***
## as.factor(DID)227       -1.735005   0.131345 -13.210  < 2e-16 ***
## as.factor(DID)228       -2.658627   0.134570 -19.756  < 2e-16 ***
## as.factor(DID)229       -2.002510   0.135447 -14.784  < 2e-16 ***
## as.factor(DID)230       -2.316566   0.132521 -17.481  < 2e-16 ***
## as.factor(DID)231       -1.754156   0.131544 -13.335  < 2e-16 ***
## as.factor(DID)232       -0.464304   0.143636  -3.233 0.001227 ** 
## as.factor(DID)233       -2.703242   0.136577 -19.793  < 2e-16 ***
## as.factor(DID)234       -1.713581   0.140235 -12.219  < 2e-16 ***
## as.factor(DID)235       -0.893286   0.138547  -6.448 1.14e-10 ***
## as.factor(DID)236       -1.030341   0.139964  -7.361 1.82e-13 ***
## as.factor(DID)237       -0.650486   0.144931  -4.488 7.18e-06 ***
## as.factor(DID)238        2.074160   0.279477   7.422 1.16e-13 ***
## as.factor(DID)239       -0.195311   0.150059  -1.302 0.193068    
## as.factor(DID)240       -0.991210   0.140867  -7.036 1.97e-12 ***
## as.factor(DID)241       -2.176730   0.133347 -16.324  < 2e-16 ***
## as.factor(DID)242       -1.056696   0.140562  -7.518 5.58e-14 ***
## as.factor(DID)243        1.248233   0.288443   4.327 1.51e-05 ***
## as.factor(DID)244       -0.458364   0.153775  -2.981 0.002875 ** 
## as.factor(DID)245        0.499207   0.178728   2.793 0.005220 ** 
## as.factor(DID)246       -1.743685   0.135781 -12.842  < 2e-16 ***
## as.factor(DID)247       -1.333756   0.143196  -9.314  < 2e-16 ***
## as.factor(DID)248       -0.907417   0.134861  -6.729 1.71e-11 ***
## as.factor(DID)249        0.158783   0.171813   0.924 0.355402    
## as.factor(DID)250       -0.712523   0.141296  -5.043 4.59e-07 ***
## as.factor(DID)251       -0.565760   0.147171  -3.844 0.000121 ***
## as.factor(DID)252       -0.898395   0.140523  -6.393 1.62e-10 ***
## as.factor(DID)253       -0.621309   0.150444  -4.130 3.63e-05 ***
## as.factor(DID)254       -1.790591   0.153669 -11.652  < 2e-16 ***
## as.factor(DID)255       -0.935352   0.147504  -6.341 2.28e-10 ***
## as.factor(DID)256        0.207917   0.154532   1.345 0.178477    
## as.factor(DID)257        0.344889   0.193597   1.781 0.074835 .  
## as.factor(DID)258       -1.948788   0.132914 -14.662  < 2e-16 ***
## as.factor(DID)259        0.759450   0.197304   3.849 0.000119 ***
## as.factor(DID)260        0.310348   0.155598   1.995 0.046092 *  
## as.factor(DID)261       -2.433519   0.130869 -18.595  < 2e-16 ***
## as.factor(DID)262       -0.326379   0.145632  -2.241 0.025018 *  
## as.factor(DID)263        0.263813   0.159259   1.657 0.097620 .  
## as.factor(DID)264        0.073832   0.151384   0.488 0.625751    
## as.factor(DID)265        0.762879   0.181253   4.209 2.57e-05 ***
## as.factor(DID)266        1.003127   0.197894   5.069 4.00e-07 ***
## as.factor(DID)267        0.666027   0.176493   3.774 0.000161 ***
## as.factor(DID)268       -0.790351   0.151366  -5.221 1.78e-07 ***
## as.factor(DID)269        1.810794   0.286952   6.310 2.78e-10 ***
## as.factor(DID)270        0.686298   0.174083   3.942 8.07e-05 ***
## as.factor(DID)271        1.364386   0.239319   5.701 1.19e-08 ***
## as.factor(DID)272        1.768055   0.279472   6.326 2.51e-10 ***
## as.factor(DID)273        0.630907   0.187810   3.359 0.000781 ***
## as.factor(DID)274        1.026168   0.252575   4.063 4.85e-05 ***
## as.factor(DID)275       -0.974477   0.140893  -6.916 4.63e-12 ***
## as.factor(DID)276        2.180771   0.279161   7.812 5.63e-15 ***
## as.factor(DID)277       -0.317574   0.206789  -1.536 0.124603    
## as.factor(DID)278       -0.451825   0.136262  -3.316 0.000914 ***
## as.factor(DID)279       -1.922434   0.134162 -14.329  < 2e-16 ***
## as.factor(DID)280       -0.736505   0.143713  -5.125 2.98e-07 ***
## as.factor(DID)281        0.019798   0.152403   0.130 0.896640    
## as.factor(DID)282       -0.670133   0.154367  -4.341 1.42e-05 ***
## as.factor(DID)284       -0.722366   0.141257  -5.114 3.16e-07 ***
## as.factor(DID)287       -0.692193   0.147107  -4.705 2.53e-06 ***
## as.factor(DID)289       -1.038543   0.135358  -7.673 1.69e-14 ***
## as.factor(DID)290       -1.134186   0.140658  -8.063 7.42e-16 ***
## as.factor(DID)315       -0.668540   0.165471  -4.040 5.34e-05 ***
## as.factor(DID)316       -1.529865   0.137377 -11.136  < 2e-16 ***
## as.factor(DID)318       -1.901524   0.138526 -13.727  < 2e-16 ***
## as.factor(DID)319       -2.613872   0.131260 -19.914  < 2e-16 ***
## as.factor(DID)320       -1.944721   0.138241 -14.068  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 212206  on 208249  degrees of freedom
## Residual deviance: 168677  on 208090  degrees of freedom
## AIC: 168997
## 
## Number of Fisher Scoring iterations: 7
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 22.909, df = 8, p-value = 0.003483
AIC
extractAIC(glm_child)
## [1]    160.0 168997.3
BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))
## [1]    160.0 170636.7
effectiveness of explanatory variables
glm_child_null <- glm(C003_01 ~ 1, family = "binomial", 
                      data = child_ica_dummy %>% filter(C001 >= 5))
anova(glm_child_null, glm_child, test = "Chisq")
multicolinearity
vif(glm_child)
##                             GVIF  Df GVIF^(1/(2*Df))
## n_children_in_household 1.155403   1        1.074897
## C002_01                 1.043050   1        1.021298
## PR004_only_01           1.049989   1        1.024690
## PR009_only_01           1.214044   1        1.101837
## PR004_PR009_both_01     1.316493   1        1.147385
## as.factor(C001)         1.105681  11        1.004577
## as.factor(DID)          1.661579 143        1.001777

GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + H002

glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy %>% 
##     filter(C001 >= 5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1606  -0.6023  -0.4740  -0.3346   2.5325  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -3.267561   0.024107 -135.54   <2e-16 ***
## n_children_in_household  0.102019   0.004052   25.18   <2e-16 ***
## C002_01                  0.831929   0.012823   64.88   <2e-16 ***
## H002_1_01                1.273672   0.019170   66.44   <2e-16 ***
## H002_2_01                0.310590   0.020780   14.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 175563  on 208249  degrees of freedom
## Residual deviance: 163262  on 208245  degrees of freedom
## AIC: 163272
## 
## Number of Fisher Scoring iterations: 5
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 104.14, df = 8, p-value < 2.2e-16

GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01

glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% 
##     filter(C001 >= 5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1965  -0.5924  -0.5127  -0.3458   2.4672  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -2.025765   0.019446 -104.17   <2e-16 ***
## n_children_in_household  0.103873   0.004048   25.66   <2e-16 ***
## C002_01                  0.824116   0.012805   64.36   <2e-16 ***
## PR004_PR009_01          -1.072905   0.013003  -82.51   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 175563  on 208249  degrees of freedom
## Residual deviance: 163492  on 208246  degrees of freedom
## AIC: 163500
## 
## Number of Fisher Scoring iterations: 5
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 62.991, df = 8, p-value = 1.203e-10

GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01

glm_child <- glm(C003_1_01 ~ 
                   n_children_in_household + 
                   C002_01 + 
                   PR004_only_01 + PR009_only_01 + PR004_PR009_both_01, 
                 family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     PR004_only_01 + PR009_only_01 + PR004_PR009_both_01, family = "binomial", 
##     data = child_ica_dummy %>% filter(C001 >= 5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1573  -0.6019  -0.4316  -0.2874   2.6351  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -1.982321   0.019437 -101.99   <2e-16 ***
## n_children_in_household  0.091439   0.004062   22.51   <2e-16 ***
## C002_01                  0.837617   0.012837   65.25   <2e-16 ***
## PR004_only_01           -0.618710   0.036280  -17.05   <2e-16 ***
## PR009_only_01           -0.755745   0.015594  -48.46   <2e-16 ***
## PR004_PR009_both_01     -1.549333   0.019266  -80.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 175563  on 208249  degrees of freedom
## Residual deviance: 161951  on 208244  degrees of freedom
## AIC: 161963
## 
## Number of Fisher Scoring iterations: 5
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 168.55, df = 8, p-value < 2.2e-16

GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID) +as.factor(C001)

glm_child <- glm(C003_1_01 ~ 
                   n_children_in_household + 
                   C002_01 + 
                   PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + 
                   as.factor(DID) + as.factor(C001), 
                 family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID) + 
##     as.factor(C001), family = "binomial", data = child_ica_dummy %>% 
##     filter(C001 >= 5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2017  -0.5564  -0.3235  -0.1480   3.6277  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -1.849141   0.133244 -13.878  < 2e-16 ***
## n_children_in_household  0.103958   0.004664  22.290  < 2e-16 ***
## C002_01                  0.999144   0.014066  71.034  < 2e-16 ***
## PR004_only_01           -0.403175   0.039899 -10.105  < 2e-16 ***
## PR009_only_01           -0.532623   0.017797 -29.927  < 2e-16 ***
## PR004_PR009_both_01     -0.963714   0.022615 -42.614  < 2e-16 ***
## as.factor(DID)147        0.205564   0.164266   1.251 0.210785    
## as.factor(DID)148       -0.828939   0.194950  -4.252 2.12e-05 ***
## as.factor(DID)149        0.168422   0.160527   1.049 0.294094    
## as.factor(DID)150        0.023630   0.159293   0.148 0.882071    
## as.factor(DID)151       -0.665946   0.199491  -3.338 0.000843 ***
## as.factor(DID)152       -0.298050   0.197139  -1.512 0.130566    
## as.factor(DID)153        0.497344   0.158172   3.144 0.001665 ** 
## as.factor(DID)154       -0.033152   0.172603  -0.192 0.847685    
## as.factor(DID)155        0.192964   0.156074   1.236 0.216325    
## as.factor(DID)156        1.614906   0.166834   9.680  < 2e-16 ***
## as.factor(DID)157       -0.301365   0.185305  -1.626 0.103881    
## as.factor(DID)158       -0.773480   0.196681  -3.933 8.40e-05 ***
## as.factor(DID)159        1.571675   0.154255  10.189  < 2e-16 ***
## as.factor(DID)160        0.392209   0.157431   2.491 0.012727 *  
## as.factor(DID)161        1.195362   0.143193   8.348  < 2e-16 ***
## as.factor(DID)162       -0.276268   0.202542  -1.364 0.172567    
## as.factor(DID)163       -0.222917   0.202380  -1.101 0.270689    
## as.factor(DID)164        0.770335   0.168918   4.560 5.11e-06 ***
## as.factor(DID)165        0.697253   0.156374   4.459 8.24e-06 ***
## as.factor(DID)166       -0.540689   0.186512  -2.899 0.003744 ** 
## as.factor(DID)167       -1.077479   0.228114  -4.723 2.32e-06 ***
## as.factor(DID)169        1.797122   0.142723  12.592  < 2e-16 ***
## as.factor(DID)170        0.116329   0.168088   0.692 0.488892    
## as.factor(DID)171       -0.167964   0.180671  -0.930 0.352544    
## as.factor(DID)172       -0.188000   0.174971  -1.074 0.282615    
## as.factor(DID)173       -0.184565   0.172638  -1.069 0.285031    
## as.factor(DID)174        0.421502   0.162740   2.590 0.009597 ** 
## as.factor(DID)175        1.206286   0.149076   8.092 5.88e-16 ***
## as.factor(DID)176       -1.580346   0.320298  -4.934 8.06e-07 ***
## as.factor(DID)177        0.235226   0.158818   1.481 0.138578    
## as.factor(DID)178       -2.532112   0.378289  -6.694 2.18e-11 ***
## as.factor(DID)179       -0.603133   0.191250  -3.154 0.001612 ** 
## as.factor(DID)180        0.089222   0.165482   0.539 0.589774    
## as.factor(DID)181       -1.537288   0.278060  -5.529 3.23e-08 ***
## as.factor(DID)182        1.451183   0.145476   9.975  < 2e-16 ***
## as.factor(DID)183        0.408925   0.160516   2.548 0.010848 *  
## as.factor(DID)184        1.287504   0.145110   8.873  < 2e-16 ***
## as.factor(DID)185       -2.373217   0.291999  -8.127 4.38e-16 ***
## as.factor(DID)186        1.598946   0.144219  11.087  < 2e-16 ***
## as.factor(DID)187        1.545379   0.145880  10.593  < 2e-16 ***
## as.factor(DID)188        1.082158   0.148089   7.307 2.72e-13 ***
## as.factor(DID)189        0.123719   0.163652   0.756 0.449656    
## as.factor(DID)190        0.984796   0.157629   6.248 4.17e-10 ***
## as.factor(DID)191        0.448639   0.164781   2.723 0.006476 ** 
## as.factor(DID)192        0.223198   0.163943   1.361 0.173376    
## as.factor(DID)193       -0.942345   0.189106  -4.983 6.26e-07 ***
## as.factor(DID)194        1.549331   0.144193  10.745  < 2e-16 ***
## as.factor(DID)195        2.058681   0.144074  14.289  < 2e-16 ***
## as.factor(DID)196       -0.482149   0.202255  -2.384 0.017132 *  
## as.factor(DID)197        1.358136   0.146099   9.296  < 2e-16 ***
## as.factor(DID)198        1.901734   0.143289  13.272  < 2e-16 ***
## as.factor(DID)199       -1.898863   0.230299  -8.245  < 2e-16 ***
## as.factor(DID)200        1.645844   0.147095  11.189  < 2e-16 ***
## as.factor(DID)202        0.972419   0.151301   6.427 1.30e-10 ***
## as.factor(DID)203        1.600055   0.142738  11.210  < 2e-16 ***
## as.factor(DID)204        0.367293   0.155241   2.366 0.017984 *  
## as.factor(DID)205        1.545918   0.143079  10.805  < 2e-16 ***
## as.factor(DID)206        1.318598   0.143909   9.163  < 2e-16 ***
## as.factor(DID)207        2.006045   0.142147  14.112  < 2e-16 ***
## as.factor(DID)208        1.100007   0.146457   7.511 5.88e-14 ***
## as.factor(DID)209        0.642938   0.160908   3.996 6.45e-05 ***
## as.factor(DID)210        1.591918   0.141432  11.256  < 2e-16 ***
## as.factor(DID)211        0.724967   0.160080   4.529 5.93e-06 ***
## as.factor(DID)212        1.303122   0.146545   8.892  < 2e-16 ***
## as.factor(DID)213        1.230974   0.144657   8.510  < 2e-16 ***
## as.factor(DID)214        1.749593   0.143375  12.203  < 2e-16 ***
## as.factor(DID)215        0.639540   0.145059   4.409 1.04e-05 ***
## as.factor(DID)216        1.823115   0.145175  12.558  < 2e-16 ***
## as.factor(DID)217        1.567133   0.144493  10.846  < 2e-16 ***
## as.factor(DID)218        1.630086   0.141505  11.520  < 2e-16 ***
## as.factor(DID)219        1.404646   0.146425   9.593  < 2e-16 ***
## as.factor(DID)220        1.859494   0.139248  13.354  < 2e-16 ***
## as.factor(DID)221        2.110932   0.143199  14.741  < 2e-16 ***
## as.factor(DID)222        1.527495   0.141950  10.761  < 2e-16 ***
## as.factor(DID)223        1.193202   0.156236   7.637 2.22e-14 ***
## as.factor(DID)224        1.810767   0.143197  12.645  < 2e-16 ***
## as.factor(DID)225        1.505207   0.145868  10.319  < 2e-16 ***
## as.factor(DID)226        1.481510   0.146355  10.123  < 2e-16 ***
## as.factor(DID)227        1.019017   0.143777   7.087 1.37e-12 ***
## as.factor(DID)228        2.404917   0.143965  16.705  < 2e-16 ***
## as.factor(DID)229        1.545111   0.146713  10.532  < 2e-16 ***
## as.factor(DID)230        1.960398   0.143007  13.708  < 2e-16 ***
## as.factor(DID)231        1.268291   0.143591   8.833  < 2e-16 ***
## as.factor(DID)232        0.209344   0.157984   1.325 0.185138    
## as.factor(DID)233        2.212538   0.146746  15.077  < 2e-16 ***
## as.factor(DID)234        1.463183   0.151296   9.671  < 2e-16 ***
## as.factor(DID)235        0.015099   0.162894   0.093 0.926149    
## as.factor(DID)236        0.579297   0.155809   3.718 0.000201 ***
## as.factor(DID)237        0.378634   0.160412   2.360 0.018256 *  
## as.factor(DID)238       -2.197426   0.308821  -7.116 1.11e-12 ***
## as.factor(DID)239       -1.265875   0.207622  -6.097 1.08e-09 ***
## as.factor(DID)240        0.766126   0.153751   4.983 6.26e-07 ***
## as.factor(DID)241        1.176999   0.145743   8.076 6.70e-16 ***
## as.factor(DID)242        0.899382   0.152594   5.894 3.77e-09 ***
## as.factor(DID)243       -1.845110   0.380184  -4.853 1.21e-06 ***
## as.factor(DID)244        0.157954   0.174603   0.905 0.365651    
## as.factor(DID)245       -0.447228   0.192528  -2.323 0.020183 *  
## as.factor(DID)246        1.503046   0.147043  10.222  < 2e-16 ***
## as.factor(DID)247        1.326002   0.153763   8.624  < 2e-16 ***
## as.factor(DID)248        0.362154   0.150207   2.411 0.015907 *  
## as.factor(DID)249       -0.374297   0.193624  -1.933 0.053222 .  
## as.factor(DID)250        0.523122   0.154500   3.386 0.000709 ***
## as.factor(DID)251        0.455282   0.160349   2.839 0.004521 ** 
## as.factor(DID)252        0.740172   0.153473   4.823 1.42e-06 ***
## as.factor(DID)253       -0.238781   0.188180  -1.269 0.204477    
## as.factor(DID)254        1.707990   0.164425  10.388  < 2e-16 ***
## as.factor(DID)255        0.690543   0.163470   4.224 2.40e-05 ***
## as.factor(DID)256       -1.012697   0.195881  -5.170 2.34e-07 ***
## as.factor(DID)257       -0.582009   0.220294  -2.642 0.008243 ** 
## as.factor(DID)258        1.385600   0.146101   9.484  < 2e-16 ***
## as.factor(DID)259       -1.010251   0.213464  -4.733 2.22e-06 ***
## as.factor(DID)260       -0.423237   0.171080  -2.474 0.013364 *  
## as.factor(DID)261        2.453180   0.140555  17.454  < 2e-16 ***
## as.factor(DID)262        0.041071   0.162030   0.253 0.799899    
## as.factor(DID)263       -0.621138   0.183416  -3.386 0.000708 ***
## as.factor(DID)264       -0.589965   0.178799  -3.300 0.000968 ***
## as.factor(DID)265       -1.036202   0.213848  -4.846 1.26e-06 ***
## as.factor(DID)266       -1.933466   0.300113  -6.442 1.18e-10 ***
## as.factor(DID)267       -1.109122   0.214611  -5.168 2.37e-07 ***
## as.factor(DID)268       -0.566712   0.214151  -2.646 0.008137 ** 
## as.factor(DID)269       -1.772714   0.299992  -5.909 3.44e-09 ***
## as.factor(DID)270       -0.666780   0.191847  -3.476 0.000510 ***
## as.factor(DID)271       -1.964836   0.330517  -5.945 2.77e-09 ***
## as.factor(DID)272       -1.835837   0.308805  -5.945 2.77e-09 ***
## as.factor(DID)273       -0.524383   0.197366  -2.657 0.007886 ** 
## as.factor(DID)274       -1.712992   0.360658  -4.750 2.04e-06 ***
## as.factor(DID)275        0.700111   0.155528   4.502 6.75e-06 ***
## as.factor(DID)276       -1.845706   0.283766  -6.504 7.80e-11 ***
## as.factor(DID)277        0.117407   0.230016   0.510 0.609749    
## as.factor(DID)278        0.132079   0.149549   0.883 0.377136    
## as.factor(DID)279        1.713579   0.144491  11.859  < 2e-16 ***
## as.factor(DID)280        0.005145   0.168870   0.030 0.975695    
## as.factor(DID)281       -0.246341   0.167741  -1.469 0.141948    
## as.factor(DID)282        0.485184   0.165041   2.940 0.003284 ** 
## as.factor(DID)284        0.539209   0.154745   3.484 0.000493 ***
## as.factor(DID)287        0.394145   0.163977   2.404 0.016232 *  
## as.factor(DID)289        0.788105   0.148014   5.325 1.01e-07 ***
## as.factor(DID)290        1.026304   0.151500   6.774 1.25e-11 ***
## as.factor(DID)315        0.509505   0.183353   2.779 0.005456 ** 
## as.factor(DID)316        1.256011   0.149396   8.407  < 2e-16 ***
## as.factor(DID)318        1.658577   0.149388  11.103  < 2e-16 ***
## as.factor(DID)319        1.851007   0.142415  12.997  < 2e-16 ***
## as.factor(DID)320        1.465126   0.151500   9.671  < 2e-16 ***
## as.factor(C001)6        -0.905357   0.025404 -35.638  < 2e-16 ***
## as.factor(C001)7        -1.470798   0.027447 -53.588  < 2e-16 ***
## as.factor(C001)8        -1.638903   0.027676 -59.218  < 2e-16 ***
## as.factor(C001)9        -1.853320   0.031677 -58.507  < 2e-16 ***
## as.factor(C001)10       -1.791283   0.028801 -62.195  < 2e-16 ***
## as.factor(C001)11       -1.808296   0.036236 -49.903  < 2e-16 ***
## as.factor(C001)12       -1.682242   0.031559 -53.305  < 2e-16 ***
## as.factor(C001)13       -1.562901   0.033728 -46.338  < 2e-16 ***
## as.factor(C001)14       -1.490416   0.032505 -45.852  < 2e-16 ***
## as.factor(C001)15       -1.300463   0.035088 -37.063  < 2e-16 ***
## as.factor(C001)16       -1.235461   0.032697 -37.785  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 175563  on 208249  degrees of freedom
## Residual deviance: 136852  on 208090  degrees of freedom
## AIC: 137172
## 
## Number of Fisher Scoring iterations: 7
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 23.808, df = 8, p-value = 0.002468
AIC
extractAIC(glm_child)
## [1]    160.0 137171.9
BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))
## [1]    160.0 138811.3
effectiveness of explanatory variables
glm_child_null <- glm(C003_1_01~1, family = "binomial", 
                      data = child_ica_dummy %>% filter(C001 >= 5))
anova(glm_child_null, glm_child, test = "Chisq")
multicolinearity
vif(glm_child)
##                             GVIF  Df GVIF^(1/(2*Df))
## n_children_in_household 1.170770   1        1.082021
## C002_01                 1.035386   1        1.017539
## PR004_only_01           1.045240   1        1.022370
## PR009_only_01           1.198179   1        1.094614
## PR004_PR009_both_01     1.298763   1        1.139633
## as.factor(DID)          1.647567 143        1.001747
## as.factor(C001)         1.096843  11        1.004210
Districts with Bigger Than 0.05

GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID)

glm_child <- glm(C003_1_01 ~ 
                   n_children_in_household + 
                   C002_01 + 
                   PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID), 
                 family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID), 
##     family = "binomial", data = child_ica_dummy %>% filter(C001 >= 
##         5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.5765  -0.5979  -0.3583  -0.1691   3.5287  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -3.088711   0.130278 -23.709  < 2e-16 ***
## n_children_in_household  0.073889   0.004537  16.285  < 2e-16 ***
## C002_01                  0.973996   0.013600  71.618  < 2e-16 ***
## PR004_only_01           -0.366037   0.038712  -9.455  < 2e-16 ***
## PR009_only_01           -0.495777   0.017224 -28.783  < 2e-16 ***
## PR004_PR009_both_01     -0.863551   0.021895 -39.441  < 2e-16 ***
## as.factor(DID)147        0.267551   0.161194   1.660 0.096953 .  
## as.factor(DID)148       -0.745217   0.192233  -3.877 0.000106 ***
## as.factor(DID)149        0.221378   0.157797   1.403 0.160639    
## as.factor(DID)150        0.158111   0.156256   1.012 0.311599    
## as.factor(DID)151       -0.630034   0.196734  -3.202 0.001363 ** 
## as.factor(DID)152       -0.254627   0.194159  -1.311 0.189711    
## as.factor(DID)153        0.582818   0.154947   3.761 0.000169 ***
## as.factor(DID)154       -0.004070   0.169745  -0.024 0.980870    
## as.factor(DID)155        0.308074   0.152964   2.014 0.044006 *  
## as.factor(DID)156        1.595035   0.162716   9.803  < 2e-16 ***
## as.factor(DID)157       -0.212763   0.182336  -1.167 0.243260    
## as.factor(DID)158       -0.758062   0.194441  -3.899 9.67e-05 ***
## as.factor(DID)159        1.547324   0.151171  10.236  < 2e-16 ***
## as.factor(DID)160        0.494293   0.154313   3.203 0.001359 ** 
## as.factor(DID)161        1.236903   0.140397   8.810  < 2e-16 ***
## as.factor(DID)162       -0.255313   0.199635  -1.279 0.200933    
## as.factor(DID)163       -0.188180   0.199159  -0.945 0.344723    
## as.factor(DID)164        0.763991   0.165534   4.615 3.93e-06 ***
## as.factor(DID)165        0.720604   0.153284   4.701 2.59e-06 ***
## as.factor(DID)166       -0.402252   0.183436  -2.193 0.028316 *  
## as.factor(DID)167       -1.022371   0.225607  -4.532 5.85e-06 ***
## as.factor(DID)169        1.860780   0.139912  13.300  < 2e-16 ***
## as.factor(DID)170        0.231021   0.164777   1.402 0.160908    
## as.factor(DID)171       -0.094650   0.177401  -0.534 0.593660    
## as.factor(DID)172       -0.074935   0.172033  -0.436 0.663138    
## as.factor(DID)173       -0.128817   0.169806  -0.759 0.448084    
## as.factor(DID)174        0.552311   0.159535   3.462 0.000536 ***
## as.factor(DID)175        1.254975   0.146069   8.592  < 2e-16 ***
## as.factor(DID)176       -1.396998   0.318036  -4.393 1.12e-05 ***
## as.factor(DID)177        0.320148   0.155937   2.053 0.040066 *  
## as.factor(DID)178       -2.419262   0.376931  -6.418 1.38e-10 ***
## as.factor(DID)179       -0.430290   0.188198  -2.286 0.022233 *  
## as.factor(DID)180        0.192095   0.162529   1.182 0.237239    
## as.factor(DID)181       -1.535052   0.276232  -5.557 2.74e-08 ***
## as.factor(DID)182        1.540280   0.142358  10.820  < 2e-16 ***
## as.factor(DID)183        0.454729   0.157773   2.882 0.003950 ** 
## as.factor(DID)184        1.354138   0.142222   9.521  < 2e-16 ***
## as.factor(DID)185       -2.206682   0.289773  -7.615 2.63e-14 ***
## as.factor(DID)186        1.683653   0.141254  11.919  < 2e-16 ***
## as.factor(DID)187        1.612459   0.142726  11.298  < 2e-16 ***
## as.factor(DID)188        1.164003   0.145125   8.021 1.05e-15 ***
## as.factor(DID)189        0.150156   0.160658   0.935 0.349980    
## as.factor(DID)190        1.051771   0.153881   6.835 8.20e-12 ***
## as.factor(DID)191        0.528752   0.161748   3.269 0.001079 ** 
## as.factor(DID)192        0.479501   0.160409   2.989 0.002797 ** 
## as.factor(DID)193       -0.851517   0.186012  -4.578 4.70e-06 ***
## as.factor(DID)194        1.630212   0.141321  11.536  < 2e-16 ***
## as.factor(DID)195        2.097849   0.141286  14.848  < 2e-16 ***
## as.factor(DID)196       -0.302192   0.198932  -1.519 0.128744    
## as.factor(DID)197        1.423960   0.143141   9.948  < 2e-16 ***
## as.factor(DID)198        2.001490   0.140244  14.271  < 2e-16 ***
## as.factor(DID)199       -1.590277   0.227719  -6.983 2.88e-12 ***
## as.factor(DID)200        1.746889   0.143874  12.142  < 2e-16 ***
## as.factor(DID)202        1.103387   0.147963   7.457 8.84e-14 ***
## as.factor(DID)203        1.621913   0.139993  11.586  < 2e-16 ***
## as.factor(DID)204        0.560661   0.151963   3.689 0.000225 ***
## as.factor(DID)205        1.560604   0.140253  11.127  < 2e-16 ***
## as.factor(DID)206        1.269490   0.141246   8.988  < 2e-16 ***
## as.factor(DID)207        2.050115   0.139367  14.710  < 2e-16 ***
## as.factor(DID)208        1.146535   0.143548   7.987 1.38e-15 ***
## as.factor(DID)209        0.869728   0.156843   5.545 2.94e-08 ***
## as.factor(DID)210        1.625689   0.138828  11.710  < 2e-16 ***
## as.factor(DID)211        0.860828   0.156332   5.506 3.66e-08 ***
## as.factor(DID)212        1.293692   0.143754   8.999  < 2e-16 ***
## as.factor(DID)213        1.260507   0.141847   8.886  < 2e-16 ***
## as.factor(DID)214        1.849210   0.140470  13.164  < 2e-16 ***
## as.factor(DID)215        0.793419   0.142102   5.583 2.36e-08 ***
## as.factor(DID)216        1.764522   0.142493  12.383  < 2e-16 ***
## as.factor(DID)217        1.608667   0.141682  11.354  < 2e-16 ***
## as.factor(DID)218        1.664216   0.138699  11.999  < 2e-16 ***
## as.factor(DID)219        1.442222   0.143510  10.050  < 2e-16 ***
## as.factor(DID)220        1.896997   0.136653  13.882  < 2e-16 ***
## as.factor(DID)221        2.109676   0.140459  15.020  < 2e-16 ***
## as.factor(DID)222        1.570743   0.139188  11.285  < 2e-16 ***
## as.factor(DID)223        1.306498   0.152627   8.560  < 2e-16 ***
## as.factor(DID)224        1.881861   0.140437  13.400  < 2e-16 ***
## as.factor(DID)225        1.527166   0.143041  10.676  < 2e-16 ***
## as.factor(DID)226        1.538213   0.143424  10.725  < 2e-16 ***
## as.factor(DID)227        1.153281   0.140864   8.187 2.67e-16 ***
## as.factor(DID)228        2.351848   0.141252  16.650  < 2e-16 ***
## as.factor(DID)229        1.593976   0.143688  11.093  < 2e-16 ***
## as.factor(DID)230        2.008054   0.140239  14.319  < 2e-16 ***
## as.factor(DID)231        1.292720   0.140975   9.170  < 2e-16 ***
## as.factor(DID)232        0.303129   0.154774   1.959 0.050168 .  
## as.factor(DID)233        2.201350   0.143631  15.326  < 2e-16 ***
## as.factor(DID)234        1.501111   0.148199  10.129  < 2e-16 ***
## as.factor(DID)235        0.113731   0.160103   0.710 0.477482    
## as.factor(DID)236        0.707738   0.152881   4.629 3.67e-06 ***
## as.factor(DID)237        0.445267   0.157510   2.827 0.004700 ** 
## as.factor(DID)238       -2.169656   0.307083  -7.065 1.60e-12 ***
## as.factor(DID)239       -0.961190   0.204627  -4.697 2.64e-06 ***
## as.factor(DID)240        0.884368   0.150721   5.868 4.42e-09 ***
## as.factor(DID)241        1.496772   0.142546  10.500  < 2e-16 ***
## as.factor(DID)242        0.936450   0.149432   6.267 3.69e-10 ***
## as.factor(DID)243       -1.623716   0.378236  -4.293 1.76e-05 ***
## as.factor(DID)244        0.227218   0.171533   1.325 0.185295    
## as.factor(DID)245       -0.440961   0.189964  -2.321 0.020271 *  
## as.factor(DID)246        1.583942   0.143996  11.000  < 2e-16 ***
## as.factor(DID)247        1.405639   0.150138   9.362  < 2e-16 ***
## as.factor(DID)248        0.516925   0.147267   3.510 0.000448 ***
## as.factor(DID)249       -0.312794   0.190741  -1.640 0.101028    
## as.factor(DID)250        0.623316   0.151335   4.119 3.81e-05 ***
## as.factor(DID)251        0.483757   0.157277   3.076 0.002099 ** 
## as.factor(DID)252        0.791984   0.150479   5.263 1.42e-07 ***
## as.factor(DID)253       -0.193737   0.185236  -1.046 0.295612    
## as.factor(DID)254        1.815073   0.160570  11.304  < 2e-16 ***
## as.factor(DID)255        0.729138   0.160277   4.549 5.38e-06 ***
## as.factor(DID)256       -0.912317   0.193177  -4.723 2.33e-06 ***
## as.factor(DID)257       -0.459639   0.217414  -2.114 0.034506 *  
## as.factor(DID)258        1.416724   0.142970   9.909  < 2e-16 ***
## as.factor(DID)259       -0.749764   0.210188  -3.567 0.000361 ***
## as.factor(DID)260       -0.299417   0.168243  -1.780 0.075130 .  
## as.factor(DID)261        2.481736   0.137943  17.991  < 2e-16 ***
## as.factor(DID)262        0.138127   0.159118   0.868 0.385352    
## as.factor(DID)263       -0.473653   0.180734  -2.621 0.008775 ** 
## as.factor(DID)264       -0.489033   0.175887  -2.780 0.005429 ** 
## as.factor(DID)265       -0.968277   0.211168  -4.585 4.53e-06 ***
## as.factor(DID)266       -1.890424   0.298239  -6.339 2.32e-10 ***
## as.factor(DID)267       -1.047534   0.212302  -4.934 8.05e-07 ***
## as.factor(DID)268       -0.535530   0.211514  -2.532 0.011345 *  
## as.factor(DID)269       -1.667534   0.298043  -5.595 2.21e-08 ***
## as.factor(DID)270       -0.727408   0.189481  -3.839 0.000124 ***
## as.factor(DID)271       -1.956139   0.328997  -5.946 2.75e-09 ***
## as.factor(DID)272       -1.785517   0.307041  -5.815 6.05e-09 ***
## as.factor(DID)273       -0.436199   0.194478  -2.243 0.024902 *  
## as.factor(DID)274       -1.722504   0.358977  -4.798 1.60e-06 ***
## as.factor(DID)275        0.852233   0.151876   5.611 2.01e-08 ***
## as.factor(DID)276       -1.911637   0.282324  -6.771 1.28e-11 ***
## as.factor(DID)277        0.176444   0.226610   0.779 0.436202    
## as.factor(DID)278        0.353988   0.146447   2.417 0.015641 *  
## as.factor(DID)279        1.810321   0.141533  12.791  < 2e-16 ***
## as.factor(DID)280        0.102586   0.165898   0.618 0.536333    
## as.factor(DID)281       -0.004126   0.164396  -0.025 0.979978    
## as.factor(DID)282        0.637281   0.161196   3.953 7.70e-05 ***
## as.factor(DID)284        0.632971   0.151597   4.175 2.98e-05 ***
## as.factor(DID)287        0.482346   0.160831   2.999 0.002708 ** 
## as.factor(DID)289        0.883251   0.145391   6.075 1.24e-09 ***
## as.factor(DID)290        1.130739   0.148042   7.638 2.21e-14 ***
## as.factor(DID)315        0.463907   0.180093   2.576 0.009997 ** 
## as.factor(DID)316        1.239599   0.146404   8.467  < 2e-16 ***
## as.factor(DID)318        1.712416   0.146038  11.726  < 2e-16 ***
## as.factor(DID)319        1.860087   0.139617  13.323  < 2e-16 ***
## as.factor(DID)320        1.486619   0.148114  10.037  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 175563  on 208249  degrees of freedom
## Residual deviance: 144784  on 208101  degrees of freedom
## AIC: 145082
## 
## Number of Fisher Scoring iterations: 7
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 96.524, df = 8, p-value < 2.2e-16
AIC
extractAIC(glm_child)
## [1]    149.0 145081.9
BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))
## [1]    149.0 146608.6
effectiveness of explanatory variables
glm_child_null <- glm(C003_1_01 ~ 1, family = "binomial", 
                      data = child_ica_dummy %>% filter(C001 >= 5))
anova(glm_child_null, glm_child, test = "Chisq")
multicolinearity
vif(glm_child)
##                             GVIF  Df GVIF^(1/(2*Df))
## n_children_in_household 1.156358   1        1.075341
## C002_01                 1.029088   1        1.014440
## PR004_only_01           1.044432   1        1.021975
## PR009_only_01           1.192847   1        1.092175
## PR004_PR009_both_01     1.283116   1        1.132747
## as.factor(DID)          1.544326 143        1.001521

GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + as.factor(H006) + as.factor(DID)

glm_child <- glm(C003_1_01 ~ 
                   n_children_in_household + 
                   C002_01 + 
                   as.factor(H006) + as.factor(DID), 
                 family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     as.factor(H006) + as.factor(DID), family = "binomial", data = child_ica_dummy %>% 
##     filter(C001 >= 5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.6199  -0.5981  -0.3660  -0.1768   3.4949  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -3.721591   0.134213 -27.729  < 2e-16 ***
## n_children_in_household  0.086422   0.004525  19.101  < 2e-16 ***
## C002_01                  0.959674   0.013615  70.488  < 2e-16 ***
## as.factor(H006)0         0.320610   0.015132  21.188  < 2e-16 ***
## as.factor(DID)147        0.373850   0.164530   2.272 0.023072 *  
## as.factor(DID)148       -0.687998   0.195054  -3.527 0.000420 ***
## as.factor(DID)149        0.311001   0.161194   1.929 0.053686 .  
## as.factor(DID)150        0.255117   0.159758   1.597 0.110288    
## as.factor(DID)151       -0.645914   0.200582  -3.220 0.001281 ** 
## as.factor(DID)152       -0.228736   0.196910  -1.162 0.245386    
## as.factor(DID)153        0.708896   0.158507   4.472 7.74e-06 ***
## as.factor(DID)154       -0.013057   0.173296  -0.075 0.939939    
## as.factor(DID)155        0.404614   0.156765   2.581 0.009851 ** 
## as.factor(DID)156        1.485768   0.166714   8.912  < 2e-16 ***
## as.factor(DID)157       -0.106205   0.185234  -0.573 0.566404    
## as.factor(DID)158       -0.678217   0.197140  -3.440 0.000581 ***
## as.factor(DID)159        1.507692   0.154714   9.745  < 2e-16 ***
## as.factor(DID)160        0.610088   0.157789   3.866 0.000110 ***
## as.factor(DID)161        1.240051   0.144499   8.582  < 2e-16 ***
## as.factor(DID)162       -0.340061   0.202365  -1.680 0.092873 .  
## as.factor(DID)163       -0.230785   0.201753  -1.144 0.252665    
## as.factor(DID)164        0.512950   0.172153   2.980 0.002886 ** 
## as.factor(DID)165        0.776188   0.157144   4.939 7.84e-07 ***
## as.factor(DID)166       -0.235220   0.186355  -1.262 0.206873    
## as.factor(DID)167       -0.964749   0.227868  -4.234 2.30e-05 ***
## as.factor(DID)169        2.175578   0.143735  15.136  < 2e-16 ***
## as.factor(DID)170        0.308901   0.168054   1.838 0.066047 .  
## as.factor(DID)171       -0.061331   0.181487  -0.338 0.735412    
## as.factor(DID)172        0.034519   0.175134   0.197 0.843748    
## as.factor(DID)173        0.072958   0.172862   0.422 0.672982    
## as.factor(DID)174        0.672010   0.162819   4.127 3.67e-05 ***
## as.factor(DID)175        1.358325   0.149810   9.067  < 2e-16 ***
## as.factor(DID)176       -1.604798   0.319693  -5.020 5.17e-07 ***
## as.factor(DID)177        0.355006   0.159880   2.220 0.026388 *  
## as.factor(DID)178       -2.556271   0.378461  -6.754 1.43e-11 ***
## as.factor(DID)179       -0.379835   0.191004  -1.989 0.046743 *  
## as.factor(DID)180        0.410080   0.166009   2.470 0.013503 *  
## as.factor(DID)181       -1.611553   0.278145  -5.794 6.88e-09 ***
## as.factor(DID)182        1.672374   0.146282  11.433  < 2e-16 ***
## as.factor(DID)183        0.632183   0.161119   3.924 8.72e-05 ***
## as.factor(DID)184        1.549160   0.146027  10.609  < 2e-16 ***
## as.factor(DID)185       -1.939592   0.291595  -6.652 2.90e-11 ***
## as.factor(DID)186        1.845831   0.145226  12.710  < 2e-16 ***
## as.factor(DID)187        1.809977   0.146568  12.349  < 2e-16 ***
## as.factor(DID)188        1.290672   0.148983   8.663  < 2e-16 ***
## as.factor(DID)189        0.285890   0.164870   1.734 0.082912 .  
## as.factor(DID)190        1.137408   0.157498   7.222 5.13e-13 ***
## as.factor(DID)191        0.706168   0.164957   4.281 1.86e-05 ***
## as.factor(DID)192        0.432183   0.164289   2.631 0.008523 ** 
## as.factor(DID)193       -0.532890   0.190478  -2.798 0.005148 ** 
## as.factor(DID)194        1.824593   0.145176  12.568  < 2e-16 ***
## as.factor(DID)195        2.212446   0.145354  15.221  < 2e-16 ***
## as.factor(DID)196       -0.310973   0.201625  -1.542 0.122992    
## as.factor(DID)197        1.549356   0.147030  10.538  < 2e-16 ***
## as.factor(DID)198        2.263674   0.144074  15.712  < 2e-16 ***
## as.factor(DID)199       -1.275408   0.230122  -5.542 2.99e-08 ***
## as.factor(DID)200        1.974083   0.147714  13.364  < 2e-16 ***
## as.factor(DID)202        1.294832   0.151600   8.541  < 2e-16 ***
## as.factor(DID)203        1.682469   0.143998  11.684  < 2e-16 ***
## as.factor(DID)204        0.810130   0.155538   5.209 1.90e-07 ***
## as.factor(DID)205        1.684444   0.144143  11.686  < 2e-16 ***
## as.factor(DID)206        1.550575   0.146961  10.551  < 2e-16 ***
## as.factor(DID)207        2.530467   0.143200  17.671  < 2e-16 ***
## as.factor(DID)208        1.471103   0.147205   9.994  < 2e-16 ***
## as.factor(DID)209        1.074390   0.160971   6.674 2.48e-11 ***
## as.factor(DID)210        2.107355   0.142714  14.766  < 2e-16 ***
## as.factor(DID)211        1.053142   0.159837   6.589 4.43e-11 ***
## as.factor(DID)212        1.577562   0.147757  10.677  < 2e-16 ***
## as.factor(DID)213        1.484127   0.145787  10.180  < 2e-16 ***
## as.factor(DID)214        2.206505   0.144248  15.297  < 2e-16 ***
## as.factor(DID)215        1.254658   0.145937   8.597  < 2e-16 ***
## as.factor(DID)216        1.962306   0.146336  13.410  < 2e-16 ***
## as.factor(DID)217        2.053721   0.145840  14.082  < 2e-16 ***
## as.factor(DID)218        1.727461   0.142972  12.082  < 2e-16 ***
## as.factor(DID)219        1.756289   0.147591  11.900  < 2e-16 ***
## as.factor(DID)220        2.152511   0.140709  15.298  < 2e-16 ***
## as.factor(DID)221        2.427731   0.144775  16.769  < 2e-16 ***
## as.factor(DID)222        1.803915   0.143144  12.602  < 2e-16 ***
## as.factor(DID)223        1.569790   0.156253  10.046  < 2e-16 ***
## as.factor(DID)224        2.078743   0.144380  14.398  < 2e-16 ***
## as.factor(DID)225        1.592242   0.147191  10.817  < 2e-16 ***
## as.factor(DID)226        1.896638   0.147327  12.874  < 2e-16 ***
## as.factor(DID)227        1.631808   0.144732  11.275  < 2e-16 ***
## as.factor(DID)228        2.842546   0.146252  19.436  < 2e-16 ***
## as.factor(DID)229        1.875530   0.147839  12.686  < 2e-16 ***
## as.factor(DID)230        2.324956   0.144045  16.140  < 2e-16 ***
## as.factor(DID)231        1.605670   0.144866  11.084  < 2e-16 ***
## as.factor(DID)232        0.425844   0.159006   2.678 0.007403 ** 
## as.factor(DID)233        2.550016   0.147287  17.313  < 2e-16 ***
## as.factor(DID)234        1.777228   0.152827  11.629  < 2e-16 ***
## as.factor(DID)235        0.281495   0.163638   1.720 0.085392 .  
## as.factor(DID)236        0.815723   0.157297   5.186 2.15e-07 ***
## as.factor(DID)237        0.621455   0.160877   3.863 0.000112 ***
## as.factor(DID)238       -1.989754   0.308784  -6.444 1.16e-10 ***
## as.factor(DID)239       -0.813200   0.207155  -3.926 8.65e-05 ***
## as.factor(DID)240        0.991898   0.154429   6.423 1.34e-10 ***
## as.factor(DID)241        1.737582   0.146439  11.866  < 2e-16 ***
## as.factor(DID)242        1.223177   0.152933   7.998 1.26e-15 ***
## as.factor(DID)243       -1.783268   0.379548  -4.698 2.62e-06 ***
## as.factor(DID)244        0.126585   0.174664   0.725 0.468617    
## as.factor(DID)245       -0.274783   0.192729  -1.426 0.153941    
## as.factor(DID)246        1.642066   0.147825  11.108  < 2e-16 ***
## as.factor(DID)247        1.739968   0.153749  11.317  < 2e-16 ***
## as.factor(DID)248        0.850994   0.151006   5.635 1.75e-08 ***
## as.factor(DID)249       -0.030823   0.193493  -0.159 0.873435    
## as.factor(DID)250        0.841096   0.154950   5.428 5.69e-08 ***
## as.factor(DID)251        0.659748   0.160661   4.106 4.02e-05 ***
## as.factor(DID)252        1.016486   0.154010   6.600 4.11e-11 ***
## as.factor(DID)253       -0.205772   0.188125  -1.094 0.274040    
## as.factor(DID)254        2.174153   0.164308  13.232  < 2e-16 ***
## as.factor(DID)255        0.785862   0.163524   4.806 1.54e-06 ***
## as.factor(DID)256       -0.856276   0.201658  -4.246 2.17e-05 ***
## as.factor(DID)257       -0.577349   0.221811  -2.603 0.009244 ** 
## as.factor(DID)258        1.427925   0.146683   9.735  < 2e-16 ***
## as.factor(DID)259       -0.380349   0.214338  -1.775 0.075976 .  
## as.factor(DID)260       -0.282441   0.171883  -1.643 0.100339    
## as.factor(DID)261        2.748235   0.142027  19.350  < 2e-16 ***
## as.factor(DID)262        0.326642   0.162485   2.010 0.044401 *  
## as.factor(DID)263       -0.329459   0.184333  -1.787 0.073889 .  
## as.factor(DID)264       -0.391696   0.179051  -2.188 0.028697 *  
## as.factor(DID)265       -0.795216   0.213648  -3.722 0.000198 ***
## as.factor(DID)266       -1.844336   0.299942  -6.149 7.80e-10 ***
## as.factor(DID)267       -1.116769   0.214833  -5.198 2.01e-07 ***
## as.factor(DID)268       -0.649688   0.215653  -3.013 0.002590 ** 
## as.factor(DID)269       -2.013211   0.308815  -6.519 7.07e-11 ***
## as.factor(DID)270       -0.592870   0.192305  -3.083 0.002049 ** 
## as.factor(DID)271       -2.157673   0.330584  -6.527 6.72e-11 ***
## as.factor(DID)272       -2.031639   0.308746  -6.580 4.70e-11 ***
## as.factor(DID)273       -0.637853   0.198236  -3.218 0.001292 ** 
## as.factor(DID)274       -1.932309   0.360480  -5.360 8.31e-08 ***
## as.factor(DID)275        0.891903   0.155347   5.741 9.39e-09 ***
## as.factor(DID)276       -2.056423   0.284225  -7.235 4.65e-13 ***
## as.factor(DID)277        0.086716   0.231224   0.375 0.707637    
## as.factor(DID)278        0.537303   0.150240   3.576 0.000348 ***
## as.factor(DID)279        2.026590   0.145572  13.922  < 2e-16 ***
## as.factor(DID)280        0.165453   0.169033   0.979 0.327666    
## as.factor(DID)281        0.015086   0.167710   0.090 0.928326    
## as.factor(DID)282        0.614022   0.164727   3.728 0.000193 ***
## as.factor(DID)284        0.840550   0.155481   5.406 6.44e-08 ***
## as.factor(DID)287        0.483301   0.166195   2.908 0.003637 ** 
## as.factor(DID)289        0.960635   0.149138   6.441 1.19e-10 ***
## as.factor(DID)290        1.352580   0.151762   8.913  < 2e-16 ***
## as.factor(DID)315        0.419144   0.182905   2.292 0.021929 *  
## as.factor(DID)316        1.451766   0.150023   9.677  < 2e-16 ***
## as.factor(DID)318        2.127577   0.149675  14.215  < 2e-16 ***
## as.factor(DID)319        2.147538   0.143462  14.969  < 2e-16 ***
## as.factor(DID)320        1.618863   0.151783  10.666  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 173392  on 205841  degrees of freedom
## Residual deviance: 144415  on 205695  degrees of freedom
##   (2408 observations deleted due to missingness)
## AIC: 144709
## 
## Number of Fisher Scoring iterations: 7
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 90.634, df = 8, p-value = 3.331e-16

GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + as.factor(H004) + as.factor(DID)

glm_child <- glm(C003_1_01 ~ 
                   n_children_in_household + 
                   C002_01 + 
                   as.factor(H004) + as.factor(DID), 
                 family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))

ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
## 
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 + 
##     as.factor(H004) + as.factor(DID), family = "binomial", data = child_ica_dummy %>% 
##     filter(C001 >= 5))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7811  -0.5924  -0.3638  -0.1775   3.4743  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -3.674284   0.135274 -27.162  < 2e-16 ***
## n_children_in_household  0.082739   0.004537  18.236  < 2e-16 ***
## C002_01                  0.962566   0.013642  70.558  < 2e-16 ***
## as.factor(H004)0         0.585211   0.019488  30.029  < 2e-16 ***
## as.factor(DID)147        0.369970   0.165457   2.236 0.025348 *  
## as.factor(DID)148       -0.661002   0.196811  -3.359 0.000783 ***
## as.factor(DID)149        0.302150   0.162119   1.864 0.062356 .  
## as.factor(DID)150        0.348154   0.160594   2.168 0.030165 *  
## as.factor(DID)151       -0.616992   0.200161  -3.082 0.002053 ** 
## as.factor(DID)152       -0.244668   0.197664  -1.238 0.215791    
## as.factor(DID)153        0.712930   0.159347   4.474 7.67e-06 ***
## as.factor(DID)154       -0.026813   0.174504  -0.154 0.877882    
## as.factor(DID)155        0.445712   0.157907   2.823 0.004763 ** 
## as.factor(DID)156        1.454625   0.167529   8.683  < 2e-16 ***
## as.factor(DID)157       -0.123912   0.185997  -0.666 0.505280    
## as.factor(DID)158       -0.659186   0.197880  -3.331 0.000865 ***
## as.factor(DID)159        1.527356   0.155688   9.810  < 2e-16 ***
## as.factor(DID)160        0.590042   0.159326   3.703 0.000213 ***
## as.factor(DID)161        1.305486   0.145482   8.973  < 2e-16 ***
## as.factor(DID)162       -0.394190   0.205592  -1.917 0.055194 .  
## as.factor(DID)163       -0.218770   0.202365  -1.081 0.279668    
## as.factor(DID)164        0.443789   0.175903   2.523 0.011639 *  
## as.factor(DID)165        0.724727   0.158008   4.587 4.50e-06 ***
## as.factor(DID)166       -0.234487   0.187135  -1.253 0.210194    
## as.factor(DID)167       -1.002185   0.228499  -4.386 1.15e-05 ***
## as.factor(DID)169        1.914266   0.145230  13.181  < 2e-16 ***
## as.factor(DID)170        0.305500   0.169275   1.805 0.071112 .  
## as.factor(DID)171        0.008159   0.181215   0.045 0.964089    
## as.factor(DID)172        0.023677   0.175962   0.135 0.892960    
## as.factor(DID)173        0.049177   0.173720   0.283 0.777115    
## as.factor(DID)174        0.638238   0.163821   3.896 9.78e-05 ***
## as.factor(DID)175        1.327014   0.150792   8.800  < 2e-16 ***
## as.factor(DID)176       -1.587351   0.320114  -4.959 7.10e-07 ***
## as.factor(DID)177        0.318071   0.160486   1.982 0.047487 *  
## as.factor(DID)178       -2.524114   0.378846  -6.663 2.69e-11 ***
## as.factor(DID)179       -0.336789   0.191707  -1.757 0.078954 .  
## as.factor(DID)180        0.386345   0.166872   2.315 0.020601 *  
## as.factor(DID)181       -1.566604   0.278646  -5.622 1.89e-08 ***
## as.factor(DID)182        1.572877   0.147362  10.674  < 2e-16 ***
## as.factor(DID)183        0.547969   0.162730   3.367 0.000759 ***
## as.factor(DID)184        1.491184   0.147253  10.127  < 2e-16 ***
## as.factor(DID)185       -1.942884   0.292094  -6.652 2.90e-11 ***
## as.factor(DID)186        1.546274   0.146936  10.523  < 2e-16 ***
## as.factor(DID)187        1.707959   0.147791  11.557  < 2e-16 ***
## as.factor(DID)188        1.338757   0.149816   8.936  < 2e-16 ***
## as.factor(DID)189        0.304896   0.165085   1.847 0.064760 .  
## as.factor(DID)190        1.236196   0.158110   7.819 5.34e-15 ***
## as.factor(DID)191        0.730086   0.165890   4.401 1.08e-05 ***
## as.factor(DID)192        0.522978   0.165131   3.167 0.001540 ** 
## as.factor(DID)193       -0.539562   0.191229  -2.822 0.004779 ** 
## as.factor(DID)194        1.868904   0.146231  12.780  < 2e-16 ***
## as.factor(DID)195        2.087176   0.146458  14.251  < 2e-16 ***
## as.factor(DID)196       -0.213436   0.202245  -1.055 0.291273    
## as.factor(DID)197        1.594481   0.147935  10.778  < 2e-16 ***
## as.factor(DID)198        2.183850   0.145189  15.041  < 2e-16 ***
## as.factor(DID)199       -1.437922   0.231008  -6.225 4.83e-10 ***
## as.factor(DID)200        1.888885   0.148763  12.697  < 2e-16 ***
## as.factor(DID)202        1.185039   0.152863   7.752 9.02e-15 ***
## as.factor(DID)203        1.711379   0.144984  11.804  < 2e-16 ***
## as.factor(DID)204        0.712449   0.156867   4.542 5.58e-06 ***
## as.factor(DID)205        1.692428   0.145115  11.663  < 2e-16 ***
## as.factor(DID)206        1.288826   0.148589   8.674  < 2e-16 ***
## as.factor(DID)207        2.538284   0.144338  17.586  < 2e-16 ***
## as.factor(DID)208        1.483712   0.148214  10.011  < 2e-16 ***
## as.factor(DID)209        0.780425   0.162493   4.803 1.56e-06 ***
## as.factor(DID)210        2.085609   0.143912  14.492  < 2e-16 ***
## as.factor(DID)211        1.065433   0.160860   6.623 3.51e-11 ***
## as.factor(DID)212        1.503263   0.148792  10.103  < 2e-16 ***
## as.factor(DID)213        1.425913   0.146956   9.703  < 2e-16 ***
## as.factor(DID)214        2.246881   0.145209  15.473  < 2e-16 ***
## as.factor(DID)215        1.209923   0.146917   8.235  < 2e-16 ***
## as.factor(DID)216        1.992841   0.147446  13.516  < 2e-16 ***
## as.factor(DID)217        1.974725   0.146541  13.476  < 2e-16 ***
## as.factor(DID)218        1.558420   0.144335  10.797  < 2e-16 ***
## as.factor(DID)219        1.733668   0.148800  11.651  < 2e-16 ***
## as.factor(DID)220        2.268518   0.141647  16.015  < 2e-16 ***
## as.factor(DID)221        2.366030   0.145894  16.217  < 2e-16 ***
## as.factor(DID)222        1.855652   0.144094  12.878  < 2e-16 ***
## as.factor(DID)223        1.318116   0.157834   8.351  < 2e-16 ***
## as.factor(DID)224        2.149526   0.145269  14.797  < 2e-16 ***
## as.factor(DID)225        1.478411   0.148462   9.958  < 2e-16 ***
## as.factor(DID)226        1.607432   0.148898  10.796  < 2e-16 ***
## as.factor(DID)227        1.663849   0.145610  11.427  < 2e-16 ***
## as.factor(DID)228        2.769627   0.146804  18.866  < 2e-16 ***
## as.factor(DID)229        1.680662   0.149313  11.256  < 2e-16 ***
## as.factor(DID)230        2.319949   0.145041  15.995  < 2e-16 ***
## as.factor(DID)231        1.677526   0.145764  11.509  < 2e-16 ***
## as.factor(DID)232        0.235262   0.160262   1.468 0.142109    
## as.factor(DID)233        2.486440   0.148435  16.751  < 2e-16 ***
## as.factor(DID)234        1.478804   0.153939   9.606  < 2e-16 ***
## as.factor(DID)235        0.231326   0.164436   1.407 0.159491    
## as.factor(DID)236        0.749374   0.158141   4.739 2.15e-06 ***
## as.factor(DID)237        0.598388   0.162327   3.686 0.000228 ***
## as.factor(DID)238       -1.974512   0.309256  -6.385 1.72e-10 ***
## as.factor(DID)239       -0.779025   0.207857  -3.748 0.000178 ***
## as.factor(DID)240        0.977693   0.155282   6.296 3.05e-10 ***
## as.factor(DID)241        1.429672   0.148115   9.652  < 2e-16 ***
## as.factor(DID)242        1.147270   0.155268   7.389 1.48e-13 ***
## as.factor(DID)243       -1.808804   0.379924  -4.761 1.93e-06 ***
## as.factor(DID)244        0.154513   0.175891   0.878 0.379695    
## as.factor(DID)245       -0.294640   0.193545  -1.522 0.127926    
## as.factor(DID)246        1.634134   0.148793  10.983  < 2e-16 ***
## as.factor(DID)247        1.699415   0.154817  10.977  < 2e-16 ***
## as.factor(DID)248        0.689497   0.152174   4.531 5.87e-06 ***
## as.factor(DID)249       -0.087501   0.194440  -0.450 0.652699    
## as.factor(DID)250        0.813255   0.157793   5.154 2.55e-07 ***
## as.factor(DID)251        0.538697   0.162533   3.314 0.000918 ***
## as.factor(DID)252        1.031739   0.154952   6.658 2.77e-11 ***
## as.factor(DID)253       -0.155311   0.188890  -0.822 0.410947    
## as.factor(DID)254        1.976693   0.165921  11.913  < 2e-16 ***
## as.factor(DID)255        0.774559   0.164424   4.711 2.47e-06 ***
## as.factor(DID)256       -0.792758   0.201138  -3.941 8.10e-05 ***
## as.factor(DID)257       -0.515138   0.220372  -2.338 0.019409 *  
## as.factor(DID)258        1.438077   0.147761   9.732  < 2e-16 ***
## as.factor(DID)259       -0.776860   0.215533  -3.604 0.000313 ***
## as.factor(DID)260       -0.180933   0.172356  -1.050 0.293829    
## as.factor(DID)261        2.821725   0.143016  19.730  < 2e-16 ***
## as.factor(DID)262        0.348119   0.163394   2.131 0.033126 *  
## as.factor(DID)263       -0.260958   0.184449  -1.415 0.157127    
## as.factor(DID)264       -0.277613   0.179761  -1.544 0.122505    
## as.factor(DID)265       -0.764156   0.214315  -3.566 0.000363 ***
## as.factor(DID)266       -1.854871   0.300441  -6.174 6.67e-10 ***
## as.factor(DID)267       -1.144558   0.215498  -5.311 1.09e-07 ***
## as.factor(DID)268       -0.618150   0.214597  -2.881 0.003970 ** 
## as.factor(DID)269       -1.919097   0.300248  -6.392 1.64e-10 ***
## as.factor(DID)270       -0.717553   0.194149  -3.696 0.000219 ***
## as.factor(DID)271       -2.184105   0.331025  -6.598 4.17e-11 ***
## as.factor(DID)272       -2.009316   0.309241  -6.498 8.16e-11 ***
## as.factor(DID)273       -0.634134   0.198950  -3.187 0.001436 ** 
## as.factor(DID)274       -1.910088   0.360839  -5.293 1.20e-07 ***
## as.factor(DID)275        0.864599   0.156152   5.537 3.08e-08 ***
## as.factor(DID)276       -2.058385   0.284731  -7.229 4.86e-13 ***
## as.factor(DID)277        0.099404   0.229441   0.433 0.664836    
## as.factor(DID)278        0.560424   0.151118   3.709 0.000208 ***
## as.factor(DID)279        1.930761   0.146781  13.154  < 2e-16 ***
## as.factor(DID)280        0.211374   0.169877   1.244 0.213396    
## as.factor(DID)281        0.089322   0.168748   0.529 0.596585    
## as.factor(DID)282        0.826767   0.165343   5.000 5.72e-07 ***
## as.factor(DID)284        0.934917   0.156276   5.982 2.20e-09 ***
## as.factor(DID)287        0.675127   0.166211   4.062 4.87e-05 ***
## as.factor(DID)289        1.065284   0.149978   7.103 1.22e-12 ***
## as.factor(DID)290        1.044960   0.153164   6.823 8.95e-12 ***
## as.factor(DID)315        0.441218   0.183732   2.401 0.016332 *  
## as.factor(DID)316        1.442042   0.151232   9.535  < 2e-16 ***
## as.factor(DID)318        1.983840   0.151014  13.137  < 2e-16 ***
## as.factor(DID)319        2.115026   0.144608  14.626  < 2e-16 ***
## as.factor(DID)320        1.642749   0.152956  10.740  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 173453  on 205782  degrees of freedom
## Residual deviance: 144007  on 205636  degrees of freedom
##   (2467 observations deleted due to missingness)
## AIC: 144301
## 
## Number of Fisher Scoring iterations: 7
## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed
## NULL
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))
## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  glm_child$y, fitted(glm_child)
## X-squared = 80.463, df = 8, p-value = 3.941e-14

memo

fit the model

glm_child <- glm(C003_01~ C001 + C002_01 + PR004_01, family = "binomial", data = child_ica_dummy)

summary(glm_child)
## 
## Call:
## glm(formula = C003_01 ~ C001 + C002_01 + PR004_01, family = "binomial", 
##     data = child_ica_dummy)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4716  -1.0691   0.6141   0.8525   1.4442  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.567479   0.012767  -44.45   <2e-16 ***
## C001         0.174066   0.001340  129.90   <2e-16 ***
## C002_01     -0.562952   0.009369  -60.09   <2e-16 ***
## PR004_01     0.788430   0.010620   74.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 299726  on 245746  degrees of freedom
## Residual deviance: 271929  on 245743  degrees of freedom
## AIC: 271937
## 
## Number of Fisher Scoring iterations: 4
exponential transformation
exp(glm_child$coefficients)
## (Intercept)        C001     C002_01    PR004_01 
##   0.5669529   1.1901343   0.5695255   2.1999390
confidence interval (intercept and coefficient)
confint(glm_child, level = 0.95)
## Waiting for profiling to be done...
##                  2.5 %     97.5 %
## (Intercept) -0.5925094 -0.5424624
## C001         0.1714426  0.1766953
## C002_01     -0.5813180 -0.5445920
## PR004_01     0.7676346  0.8092649
exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child, level = 0.95))
## Waiting for profiling to be done...
##                 2.5 %    97.5 %
## (Intercept) 0.5529380 0.5813151
## C001        1.1870160 1.1932675
## C002_01     0.5591609 0.5800784
## PR004_01    2.1546637 2.2462561
AIC
extractAIC(glm_child)
## [1]      4.0 271937.4
BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))
## [1]      4.0 271979.1
effectiveness of explanatory variables
glm_child_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy)
anova(glm_child_null, glm_child, test = "Chisq")
variables selection
step(glm_child_null, direction = "both", 
     scope = (~ C001 + C002_01 + PR004_01 + PR009_01))
## Start:  AIC=299727.8
## C003_01 ~ 1
## 
##            Df Deviance    AIC
## + C001      1   281139 281143
## + PR009_01  1   293719 293723
## + PR004_01  1   295038 295042
## + C002_01   1   295943 295947
## <none>          299726 299728
## 
## Step:  AIC=281142.5
## C003_01 ~ C001
## 
##            Df Deviance    AIC
## + PR009_01  1   274422 274428
## + PR004_01  1   275560 275566
## + C002_01   1   277803 277809
## <none>          281139 281143
## - C001      1   299726 299728
## 
## Step:  AIC=274428
## C003_01 ~ C001 + PR009_01
## 
##            Df Deviance    AIC
## + C002_01   1   270765 270773
## + PR004_01  1   272824 272832
## <none>          274422 274428
## - PR009_01  1   281139 281143
## - C001      1   293719 293723
## 
## Step:  AIC=270773.3
## C003_01 ~ C001 + PR009_01 + C002_01
## 
##            Df Deviance    AIC
## + PR004_01  1   269072 269082
## <none>          270765 270773
## - C002_01   1   274422 274428
## - PR009_01  1   277803 277809
## - C001      1   289624 289630
## 
## Step:  AIC=269081.6
## C003_01 ~ C001 + PR009_01 + C002_01 + PR004_01
## 
##            Df Deviance    AIC
## <none>          269072 269082
## - PR004_01  1   270765 270773
## - PR009_01  1   271929 271937
## - C002_01   1   272824 272832
## - C001      1   288269 288277
## 
## Call:  glm(formula = C003_01 ~ C001 + PR009_01 + C002_01 + PR004_01, 
##     family = "binomial", data = child_ica_dummy)
## 
## Coefficients:
## (Intercept)         C001     PR009_01      C002_01     PR004_01  
##     -0.7815       0.1760       0.5677      -0.5762       0.4923  
## 
## Degrees of Freedom: 245746 Total (i.e. Null);  245742 Residual
## Null Deviance:       299700 
## Residual Deviance: 269100    AIC: 269100
multicolinearity
vif(glm_child)
##     C001  C002_01 PR004_01 
## 1.010465 1.004067 1.013630

Further Analysis

dists_not_fit

Sample Size
child_ica_dummy %>% 
  filter(DID %in% dists_not_fit) %>% 
  group_by(DID, C002_01) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(factor(DID), n, fill = factor(C002_01))) +
  geom_col(position = position_dodge()) +
  scale_fill_manual(values = c("darkseagreen", "burlywood")) +
  geom_text(aes(label = n), check_overlap = TRUE, size = 3, vjust = -1) +
  theme(axis.text.x = element_text(angle = 85)) +
  ggtitle("Sample Size in Each District")
## `summarise()` regrouping output by 'DID' (override with `.groups` argument)

Average Enrollment Rate
child_ica_dummy %>% 
  filter(DID %in% dists_not_fit) %>% 
  group_by(DID) %>% 
  summarize(avg_enrollment = mean(C003 == 3))
## `summarise()` ungrouping output (override with `.groups` argument)
Age, Enrollment
child_ica_dummy %>% 
  filter(DID %in% dists_not_fit) %>% 
  group_by(DID, C002_01, C001) %>% 
  mutate(avg = mean(C003 == 3)) %>% 
  ggplot(aes(C001, avg, color = factor(C002_01))) +
  geom_line() +
  facet_wrap(.~DID)

Parets Education
child_ica_dummy %>% 
  filter(DID %in% dists_not_fit) %>% 
  group_by(DID) %>% 
  summarize(parents_edu_rate = mean(PR004_PR009_01 == 1),
            mother = mean(PR004 == -1),
            father = mean(PR009 == -1))
## `summarise()` ungrouping output (override with `.groups` argument)
child_ica_dummy %>% 
  filter(DID %in% dists_not_fit) %>% 
  group_by(DID) %>% 
  mutate(parents_edu_rate = mean(PR004_PR009_01 == 1),
            mother = mean(PR004 == -1),
            father = mean(PR009 == -1)) %>% 
  ggplot(aes(factor(DID), parents_edu_rate, color = factor(RID))) +
  geom_point() +
  annotate("rect", xmin = 0.5, xmax = 28.5, ymin = 0.5, ymax = 1,
           alpha = .1, fill = "deepskyblue") +
  ggtitle("What Percent of Couples of Parents Have Educational Experience?")

child_ica_dummy %>% 
  filter(DID %in% dists_not_fit) %>% 
  group_by(DID) %>% 
  mutate(parents_edu_rate = mean(PR004_PR009_01 == 1),
            mother = mean(PR004 == -1),
            father = mean(PR009 == -1)) %>% 
  ggplot(aes(factor(DID), fill = factor(PR004_PR009_01))) +
  geom_histogram(position = position_stack(), stat = "count") +
  ggtitle("Mothers or Fathers Ever Been To School?")
## Warning: Ignoring unknown parameters: binwidth, bins, pad

child_ica_dummy %>% 
  filter(DID %in% dists_not_fit) %>% 
  group_by(DID) %>% 
  mutate(parents_edu_rate = mean(PR004_PR009_01 == 1),
            mother = mean(PR004 == -1),
            father = mean(PR009 == -1)) %>% 
  ggplot(aes(factor(DID), fill = factor(PR004_01))) +
  geom_histogram(position = position_stack(), stat = "count") +
  ggtitle("Mothers Ever Been To School?")
## Warning: Ignoring unknown parameters: binwidth, bins, pad

child_ica_dummy %>% 
  filter(DID %in% dists_not_fit) %>% 
  group_by(DID) %>% 
  mutate(parents_edu_rate = mean(PR004_PR009_01 == 1),
            mother = mean(PR004 == -1),
            father = mean(PR009 == -1)) %>% 
  ggplot(aes(factor(DID), fill = factor(PR009_01))) +
  geom_histogram(position = position_stack(), stat = "count") +
  ggtitle("Fathers Ever Been To School?")
## Warning: Ignoring unknown parameters: binwidth, bins, pad

boxplot
child_ica_dummy %>% 
  filter(DID == dists_not_fit) %>% 
  ggplot(aes(factor(DID), fill = factor(C003))) +
  geom_histogram(position = position_stack(), stat = "count") +
  xlab("Districts") +
  ggtitle("Education Status by Distrists Whose Data Don't Fit The Model Well")
## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Electricity
child_ica_dummy %>% 
  filter(DID == dists_not_fit) %>% 
  ggplot(aes(factor(DID), fill = factor(H004))) +
  geom_histogram(stat = "count", position = position_stack()) +
  ggtitle("Electricity Available in House?")
## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません
## Warning: Ignoring unknown parameters: binwidth, bins, pad

TV
child_ica_dummy %>% 
  filter(DID == dists_not_fit) %>% 
  ggplot(aes(factor(DID), fill = factor(H005))) +
  geom_histogram(stat = "count", position = position_stack()) +
  ggtitle("TV in House?")
## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません
## Warning: Ignoring unknown parameters: binwidth, bins, pad

DID == 199
child_ica_dummy %>% 
  filter(DID == 199) %>% 
  select(DNAME)
Institution Type
child_ica_dummy %>% 
  filter(DID == 199) %>% 
  ggplot(aes(factor(C006))) +
  geom_bar() +
  geom_text(stat = "count", aes(label = ..count..), vjust = -0.5) +
  ggtitle("School Type", subtitle = "1:Gov., 2:Private, 3:Madrasah, 4:Other")

Literacy
child_ica_dummy %>% 
  filter(DID == 199) %>% 
  ggplot(aes(factor(C001), fill = factor(C010))) +
  geom_bar(position = position_stack(), stat = "count") + 
  ggtitle("Literacy by Age", subtitle = "1:Beginner/Nothing, 2:Letters, 3:Words, 4:Sentences, 5:Story")

testing
child_ica_dummy %>% 
  group_by(H004) %>% 
  summarize(avg = mean(C003 == 3))
## `summarise()` ungrouping output (override with `.groups` argument)

Age and Number of Children in Each Household

child_ica_dummy %>% 
  filter(C001 >= 5) %>% 
  ggplot(aes(factor(C001), fill = factor(n_children_in_household))) +
  geom_bar(stat = "count", position = position_stack()) +
  ggtitle("Correlation Between Age and Number of Children in Each Household?")

child_ica_dummy %>% 
  filter(C001 >= 5) %>% 
  ggplot(aes(factor(C001), fill = factor(n_children_in_household))) +
  geom_bar(stat = "count", position = "fill") +
  ggtitle("Correlation Between Age and Number of Children in Each Household?", subtitle = "Proportional Bar Plot")